Pandas Profiling Report

Dataset statistics

Number of variables	23
Number of observations	11935
Missing cells	68691
Missing cells (%)	25.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	2.1 MiB
Average record size in memory	184.0 B

Variable types

Numeric	9
Categorical	12
Unsupported	2

Warnings

`PostTypeId` has constant value "1"	Constant
`CreationDate` has a high cardinality: 11934 distinct values	High cardinality
`Body` has a high cardinality: 11935 distinct values	High cardinality
`OwnerDisplayName` has a high cardinality: 1713 distinct values	High cardinality
`LastEditorDisplayName` has a high cardinality: 907 distinct values	High cardinality
`LastEditDate` has a high cardinality: 10984 distinct values	High cardinality
`LastActivityDate` has a high cardinality: 11933 distinct values	High cardinality
`Title` has a high cardinality: 11935 distinct values	High cardinality
`Tags` has a high cardinality: 10083 distinct values	High cardinality
`ClosedDate` has a high cardinality: 1369 distinct values	High cardinality
`CommunityOwnedDate` has a high cardinality: 556 distinct values	High cardinality
`ContentLicense` is highly correlated with `PostTypeId`	High correlation
`PostTypeId` is highly correlated with `ContentLicense`	High correlation
`AcceptedAnswerId` has 1421 (11.9%) missing values	Missing
`ParentId` has 11935 (100.0%) missing values	Missing
`DeletionDate` has 11935 (100.0%) missing values	Missing
`OwnerUserId` has 306 (2.6%) missing values	Missing
`OwnerDisplayName` has 9539 (79.9%) missing values	Missing
`LastEditorUserId` has 776 (6.5%) missing values	Missing
`LastEditorDisplayName` has 10220 (85.6%) missing values	Missing
`LastEditDate` has 620 (5.2%) missing values	Missing
`ClosedDate` has 10560 (88.5%) missing values	Missing
`CommunityOwnedDate` has 11379 (95.3%) missing values	Missing
`CreationDate` is uniformly distributed	Uniform
`Body` is uniformly distributed	Uniform
`OwnerDisplayName` is uniformly distributed	Uniform
`LastActivityDate` is uniformly distributed	Uniform
`Title` is uniformly distributed	Uniform
`ClosedDate` is uniformly distributed	Uniform
`CommunityOwnedDate` is uniformly distributed	Uniform
`Id` has unique values	Unique
`Body` has unique values	Unique
`Title` has unique values	Unique
`ParentId` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`DeletionDate` is an unsupported type, check if it needs cleaning or further analysis	Unsupported
`CommentCount` has 2501 (21.0%) zeros	Zeros

Reproduction

Analysis started	2021-05-21 10:06:52.424256
Analysis finished	2021-05-21 10:07:18.551828
Duration	26.13 seconds
Software version	pandas-profiling v2.10.0
Download configuration	config.yaml

Id
Real number (ℝ_≥0)

UNIQUE

Distinct	11935
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	7945389.421
Minimum	9
Maximum	63607158
Zeros	0
Zeros (%)	0.0%
Memory size	93.4 KiB

Quantile statistics

Minimum	9
5-th percentile	111760
Q1	987685
median	3964681
Q3	11182984
95-th percentile	29959399.7
Maximum	63607158
Range	63607149
Interquartile range (IQR)	10195299

Descriptive statistics

Standard deviation	9851468.285
Coefficient of variation (CV)	1.239897476
Kurtosis	3.408015073
Mean	7945389.421
Median Absolute Deviation (MAD)	3516410
Skewness	1.840408439
Sum	9.482822274 × 10¹⁰
Variance	9.705142737 × 10¹³
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1720320	1	< 0.1%
3226282	1	< 0.1%
11832914	1	< 0.1%
1460892	1	< 0.1%
5905054	1	< 0.1%
6031214	1	< 0.1%
30024353	1	< 0.1%
30669474	1	< 0.1%
4840102	1	< 0.1%
21925671	1	< 0.1%
Other values (11925)	11925	99.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
9	1	< 0.1%
11	1	< 0.1%
13	1	< 0.1%
19	1	< 0.1%
42	1	< 0.1%

Value	Count	Frequency (%)
63607158	1	< 0.1%
59006602	1	< 0.1%
58441514	1	< 0.1%
57456188	1	< 0.1%
56839307	1	< 0.1%

PostTypeId
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	93.4 KiB

1	11935

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	11935
Distinct characters	1
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Value	Count	Frequency (%)
1	11935	100.0%

Histogram of lengths of the category

Value	Count	Frequency (%)
1	11935	100.0%

Most occurring characters

Value	Count	Frequency (%)
1	11935	100.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	11935	100.0%

Most frequent character per category

Value	Count	Frequency (%)
1	11935	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	11935	100.0%

Most frequent character per script

Value	Count	Frequency (%)
1	11935	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	11935	100.0%

Most frequent character per block

Value	Count	Frequency (%)
1	11935	100.0%

AcceptedAnswerId
Real number (ℝ_≥0)

MISSING

Distinct	10514
Distinct (%)	100.0%
Missing	1421
Missing (%)	11.9%
Infinite	0
Infinite (%)	0.0%

Mean	9170627.091
Minimum	77
Maximum	63955114
Zeros	0
Zeros (%)	0.0%
Memory size	93.4 KiB

Quantile statistics

Minimum	77
5-th percentile	135206.95
Q1	1221941.75
median	4737625
Q3	13198476.25
95-th percentile	33163627.2
Maximum	63955114
Range	63955037
Interquartile range (IQR)	11976534.5

Descriptive statistics

Standard deviation	10980995.05
Coefficient of variation (CV)	1.197409397
Kurtosis	3.021260069
Mean	9170627.091
Median Absolute Deviation (MAD)	4217358.5
Skewness	1.755893514
Sum	9.641997324 × 10¹⁰
Variance	1.205822523 × 10¹⁴
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
4955297	1	< 0.1%
486663	1	< 0.1%
7975511	1	< 0.1%
33064	1	< 0.1%
2587398	1	< 0.1%
1282265	1	< 0.1%
2706537	1	< 0.1%
9255507	1	< 0.1%
10775442	1	< 0.1%
6293626	1	< 0.1%
Other values (10504)	10504	88.0%
(Missing)	1421	11.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
77	1	< 0.1%
298	1	< 0.1%
362	1	< 0.1%
396	1	< 0.1%
516	1	< 0.1%

Value	Count	Frequency (%)
63955114	1	< 0.1%
62551786	1	< 0.1%
62438464	1	< 0.1%
62196340	1	< 0.1%
61859561	1	< 0.1%

ParentId
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing	11935
Missing (%)	100.0%
Memory size	93.4 KiB

CreationDate
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	11934
Distinct (%)	> 99.9%
Missing	0
Missing (%)	0.0%
Memory size	93.4 KiB

2008-09-01 18:16:41	2
2008-10-13 21:29:50	1
2008-10-14 18:07:37	1
2010-04-29 01:41:54	1
2010-11-24 17:51:13	1
Other values (11929)	11929

Length

Max length	19
Median length	19
Mean length	19
Min length	19

Characters and Unicode

Total characters	226765
Distinct characters	13
Distinct categories	4 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	11933 ?
Unique (%)	> 99.9%

Sample

1st row	2008-09-03 06:30:31
2nd row	2011-11-29 17:30:29
3rd row	2008-09-03 20:34:33
4th row	2008-09-03 21:32:02
5th row	2013-05-20 07:53:02

Value	Count	Frequency (%)
2008-09-01 18:16:41	2	< 0.1%
2008-10-13 21:29:50	1	< 0.1%
2008-10-14 18:07:37	1	< 0.1%
2010-04-29 01:41:54	1	< 0.1%
2010-11-24 17:51:13	1	< 0.1%
2009-06-30 12:21:40	1	< 0.1%
2012-06-04 21:26:40	1	< 0.1%
2014-07-08 00:46:51	1	< 0.1%
2010-01-19 04:55:03	1	< 0.1%
2012-07-23 20:35:02	1	< 0.1%
Other values (11924)	11924	99.9%

Histogram of lengths of the category

Value	Count	Frequency (%)
2008-09-16	50	0.2%
2008-09-17	44	0.2%
2008-09-18	37	0.2%
2008-09-19	37	0.2%
2008-09-25	36	0.2%
2008-09-15	32	0.1%
2008-09-24	29	0.1%
2008-09-23	29	0.1%
2008-10-02	27	0.1%
2008-09-26	26	0.1%
Other values (13835)	23523	98.5%

Most occurring characters

Value	Count	Frequency (%)
0	44833	19.8%
1	33446	14.7%
2	29962	13.2%
-	23870	10.5%
:	23870	10.5%
	11935	5.3%
3	11602	5.1%
4	9963	4.4%
5	9801	4.3%
9	8550	3.8%
Other values (3)	18933	8.3%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	167090	73.7%
Dash Punctuation	23870	10.5%
Other Punctuation	23870	10.5%
Space Separator	11935	5.3%

Most frequent character per category

Value	Count	Frequency (%)
0	44833	26.8%
1	33446	20.0%
2	29962	17.9%
3	11602	6.9%
4	9963	6.0%
5	9801	5.9%
9	8550	5.1%
8	7573	4.5%
6	5785	3.5%
7	5575	3.3%

Value	Count	Frequency (%)
-	23870	100.0%

Value	Count	Frequency (%)
	11935	100.0%

Value	Count	Frequency (%)
:	23870	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	226765	100.0%

Most frequent character per script

Value	Count	Frequency (%)
0	44833	19.8%
1	33446	14.7%
2	29962	13.2%
-	23870	10.5%
:	23870	10.5%
	11935	5.3%
3	11602	5.1%
4	9963	4.4%
5	9801	4.3%
9	8550	3.8%
Other values (3)	18933	8.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	226765	100.0%

Most frequent character per block

Value	Count	Frequency (%)
0	44833	19.8%
1	33446	14.7%
2	29962	13.2%
-	23870	10.5%
:	23870	10.5%
	11935	5.3%
3	11602	5.1%
4	9963	4.4%
5	9801	4.3%
9	8550	3.8%
Other values (3)	18933	8.3%

DeletionDate
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing	11935
Missing (%)	100.0%
Memory size	93.4 KiB

Score
Real number (ℝ)

Distinct	1893
Distinct (%)	15.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	559.3235861
Minimum	-146
Maximum	25400
Zeros	0
Zeros (%)	0.0%
Memory size	93.4 KiB

Quantile statistics

Minimum	-146
5-th percentile	119
Q1	226
median	364
Q3	604
95-th percentile	1627.6
Maximum	25400
Range	25546
Interquartile range (IQR)	378

Descriptive statistics

Standard deviation	770.9057898
Coefficient of variation (CV)	1.378282284
Kurtosis	197.8182413
Mean	559.3235861
Median Absolute Deviation (MAD)	167
Skewness	9.709142639
Sum	6675527
Variance	594295.7367
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
264	37	0.3%
271	34	0.3%
189	34	0.3%
280	33	0.3%
203	33	0.3%
209	32	0.3%
236	31	0.3%
217	31	0.3%
207	31	0.3%
220	31	0.3%
Other values (1883)	11608	97.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-146	1	< 0.1%
12	1	< 0.1%
28	1	< 0.1%
29	1	< 0.1%
32	2	< 0.1%

Value	Count	Frequency (%)
25400	1	< 0.1%
22484	1	< 0.1%
17846	1	< 0.1%
12452	1	< 0.1%
11051	1	< 0.1%

ViewCount
Real number (ℝ_≥0)

Distinct	11844
Distinct (%)	99.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	450605.762
Minimum	4448
Maximum	9426474
Zeros	0
Zeros (%)	0.0%
Memory size	93.4 KiB

Quantile statistics

Minimum	4448
5-th percentile	47727.4
Q1	139462.5
median	281931
Q3	549259
95-th percentile	1416149.5
Maximum	9426474
Range	9422026
Interquartile range (IQR)	409796.5

Descriptive statistics

Standard deviation	554720.3136
Coefficient of variation (CV)	1.231054639
Kurtosis	33.94365368
Mean	450605.762
Median Absolute Deviation (MAD)	171456
Skewness	4.349079131
Sum	5377979769
Variance	3.077146263 × 10¹¹
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
191511	2	< 0.1%
83458	2	< 0.1%
106323	2	< 0.1%
163655	2	< 0.1%
174890	2	< 0.1%
135433	2	< 0.1%
141657	2	< 0.1%
60679	2	< 0.1%
511760	2	< 0.1%
75038	2	< 0.1%
Other values (11834)	11915	99.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
4448	1	< 0.1%
5969	1	< 0.1%
6974	1	< 0.1%
7140	1	< 0.1%
7460	1	< 0.1%

Value	Count	Frequency (%)
9426474	1	< 0.1%
8730703	1	< 0.1%
8001167	1	< 0.1%
7569705	1	< 0.1%
7181095	1	< 0.1%

Body
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	11935
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	93.4 KiB

<p>I'm trying to perform some offline maintenance (dev database restore from live backup) on my dev database, but the 'Take Offline' command via SQL Server Management Studio is performing <strong>extremely</strong> slowly - on the order of 30 minutes plus now. I am just about at my wits end and I can't seem to find any references online as to what might be causing the speed problem, or how to fix it.</p> <p>Some sites have suggested that open connections to the database cause this slowdown, but the only application that uses this database is my dev machine's IIS instance, and the service is stopped - there are no more open connections.</p> <p>What could be causing this slowdown, and what can I do to speed it up?</p>	1
<p><a href="https://stackoverflow.com/questions/292357/whats-the-difference-between-git-pull-and-git-fetch">Another question</a> says that <code>git pull</code> is like a <code>git fetch</code> + <code>git merge</code>.</p> <p>But what is the difference between <code>git pull</code> and <code>git fetch</code> + <code>git rebase</code>?</p>	1
<p>Sealed classes are described in 'Programming in Scala', but sealed traits are not. Where can I find more information about a sealed trait?</p> <p>I would like to know, if a sealed trait is the same as a sealed class? Or, if not, what are the differences? When is it a good idea to use a sealed trait (and when not)?</p>	1
<p>It is supposed to be generally preferable to use a <code>StringBuilder</code> for string concatenation in Java. Is this always the case?</p> <p>What I mean is this: Is the overhead of creating a <code>StringBuilder</code> object, calling the <code>append()</code> method and finally <code>toString()</code> already smaller then concatenating existing strings with the <code>+</code> operator for two strings, or is it only advisable for more (than two) strings?</p> <p>If there is such a threshold, what does it depend on (perhaps the string length, but in which way)?</p> <p>And finally, would you trade the readability and conciseness of the <code>+</code> concatenation for the performance of the <code>StringBuilder</code> in smaller cases like two, three or four strings?</p> <p>Explicit use of <code>StringBuilder</code> for regular concatenations is being mentioned as obsolete at <a href="https://stackoverflow.com/questions/4019180/obsolete-java-optimization-tips">obsolete Java optimization tips</a> as well as at <a href="https://stackoverflow.com/questions/2248278/java-urban-myths">Java urban myths</a>.</p>	1
<p>In terms of Java, when someone asks: </p> <blockquote> <p>what is polymorphism?</p> </blockquote> <p>Would <strong>overloading</strong> or <strong>overriding</strong> be an acceptable answer?</p> <p>I think there is a bit more to it than that. </p> <p><em>IF you had a abstract base class that defined a method with no implementation, and you defined that method in the sub class, is that still overridding?</em></p> <p>I think <strong>overloading</strong> is not the right answer for sure.</p>	1
Other values (11930)	11930

Length

Max length	24750
Median length	512
Mean length	829.1379137
Min length	23

Characters and Unicode

Total characters	9895761
Distinct characters	408
Distinct categories	22 ?
Distinct scripts	11 ?
Distinct blocks	23 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	11935 ?
Unique (%)	100.0%

Sample

1st row	<p>I found an example in the <a href="http://msdn2.microsoft.com/en-us/bb330936.aspx" rel="noreferrer">VS2008 Examples</a> for Dynamic LINQ that allows you to use a sql-like string (e.g. <code>OrderBy("Name, Age DESC"))</code> for ordering. Unfortunately, the method included only works on <code>IQueryable<T></code>. Is there any way to get this functionality on <code>IEnumerable<T></code>?</p>
2nd row	<p>RequireJS seems to do something internally that caches required javascript files. If I make a change to one of the required files, I have to rename the file in order for the changes to be applied. </p> <p>The common trick of appending a version number as a querystring param to the end of the filename does not work with requirejs <code><script src="jsfile.js?v2"></script></code> </p> <p>What I am looking for is a way to prevent this internal cacheing of RequireJS required scripts without having to rename my script files every time they are updated.</p> <p><strong>Cross-Platform Solution:</strong></p> <p>I am now using <code>urlArgs: "bust=" + (new Date()).getTime()</code> for automatic cache-busting during development and <code>urlArgs: "bust=v2"</code> for production where I increment the hard-coded version num after rolling out an updated required script.</p> <p><strong>Note:</strong></p> <p>@Dustin Getz mentioned in a recent answer that Chrome Developer Tools will drop breakpoints during debugging when Javascript files are continuously refreshed like this. One workaround is to write <code>debugger;</code> in code to trigger a breakpoint in most Javascript debuggers.</p> <p><strong>Server-Specific Solutions:</strong></p> <p>For specific solutions that may work better for your server environment such as Node or Apache, see some of the answers below.</p>
3rd row	<p>Inspired by <a href="https://devblogs.microsoft.com/oldnewthing/20080902-00/?p=21003" rel="noreferrer">Raymond Chen's post</a>, say you have a 4x4 two dimensional array, write a function that rotates it 90 degrees. Raymond links to a solution in pseudo code, but I'd like to see some real world stuff.</p> <pre><code>[1][2][3][4] [5][6][7][8] [9][0][1][2] [3][4][5][6] </code></pre> <p>Becomes:</p> <pre><code>[3][9][5][1] [4][0][6][2] [5][1][7][3] [6][2][8][4] </code></pre> <p><strong>Update</strong>: Nick's answer is the most straightforward, but is there a way to do it better than n^2? What if the matrix was 10000x10000?</p>
4th row	<p>What is the best way to get <code>IDENTITY</code> of inserted row?</p> <p>I know about <code>@@IDENTITY</code> and <code>IDENT_CURRENT</code> and <code>SCOPE_IDENTITY</code> but don't understand the pros and cons attached to each.</p> <p>Can someone please explain the differences and when I should be using each?</p>
5th row	<p>I am trying to synchronise a project that I have on in my Android Studio folder to GitHub, but I am not fully sure what to do other than adding my credentials in the options menu. Could someone give me a quick guide, please?</p>

Value	Count	Frequency (%)
<p>I'm trying to perform some offline maintenance (dev database restore from live backup) on my dev database, but the 'Take Offline' command via SQL Server Management Studio is performing <strong>extremely</strong> slowly - on the order of 30 minutes plus now. I am just about at my wits end and I can't seem to find any references online as to what might be causing the speed problem, or how to fix it.</p> <p>Some sites have suggested that open connections to the database cause this slowdown, but the only application that uses this database is my dev machine's IIS instance, and the service is stopped - there are no more open connections.</p> <p>What could be causing this slowdown, and what can I do to speed it up?</p>	1	< 0.1%
<p><a href="https://stackoverflow.com/questions/292357/whats-the-difference-between-git-pull-and-git-fetch">Another question</a> says that <code>git pull</code> is like a <code>git fetch</code> + <code>git merge</code>.</p> <p>But what is the difference between <code>git pull</code> and <code>git fetch</code> + <code>git rebase</code>?</p>	1	< 0.1%
<p>Sealed classes are described in 'Programming in Scala', but sealed traits are not. Where can I find more information about a sealed trait?</p> <p>I would like to know, if a sealed trait is the same as a sealed class? Or, if not, what are the differences? When is it a good idea to use a sealed trait (and when not)?</p>	1	< 0.1%
<p>It is supposed to be generally preferable to use a <code>StringBuilder</code> for string concatenation in Java. Is this always the case?</p> <p>What I mean is this: Is the overhead of creating a <code>StringBuilder</code> object, calling the <code>append()</code> method and finally <code>toString()</code> already smaller then concatenating existing strings with the <code>+</code> operator for two strings, or is it only advisable for more (than two) strings?</p> <p>If there is such a threshold, what does it depend on (perhaps the string length, but in which way)?</p> <p>And finally, would you trade the readability and conciseness of the <code>+</code> concatenation for the performance of the <code>StringBuilder</code> in smaller cases like two, three or four strings?</p> <p>Explicit use of <code>StringBuilder</code> for regular concatenations is being mentioned as obsolete at <a href="https://stackoverflow.com/questions/4019180/obsolete-java-optimization-tips">obsolete Java optimization tips</a> as well as at <a href="https://stackoverflow.com/questions/2248278/java-urban-myths">Java urban myths</a>.</p>	1	< 0.1%
<p>In terms of Java, when someone asks: </p> <blockquote> <p>what is polymorphism?</p> </blockquote> <p>Would <strong>overloading</strong> or <strong>overriding</strong> be an acceptable answer?</p> <p>I think there is a bit more to it than that. </p> <p><em>IF you had a abstract base class that defined a method with no implementation, and you defined that method in the sub class, is that still overridding?</em></p> <p>I think <strong>overloading</strong> is not the right answer for sure.</p>	1	< 0.1%
<p>Once upon a time, we can watch the most popular repositories (Most forked or Most watched) at this page (<a href="https://github.com/popular/watched">https://github.com/popular/watched</a>) of Github. like this:</p> <p><img src="https://i.imgur.com/nkEzfwY.png" alt="img"></p> <p>But now when you try to explore repos, you can only see the Top 25 trending repositories. like this: <a href="https://github.com/trending">https://github.com/trending</a></p> <p>Why Github change this, and is there any way to find out the list of the most popular repos?</p>	1	< 0.1%
<p>Is there a destructor for Java? I don't seem to be able to find any documentation on this. If there isn't, how can I achieve the same effect?</p> <p>To make my question more specific, I am writing an application that deals with data and the specification say that there should be a 'reset' button that brings the application back to its original just launched state. However, all data have to be 'live' unless the application is closed or reset button is pressed.</p> <p>Being usually a C/C++ programmer, I thought this would be trivial to implement. (And hence I planned to implement it last.) I structured my program such that all the 'reset-able' objects would be in the same class so that I can just destroy all 'live' objects when a reset button is pressed.</p> <p>I was thinking if all I did was just to dereference the data and wait for the garbage collector to collect them, wouldn't there be a memory leak if my user repeatedly entered data and pressed the reset button? I was also thinking since Java is quite mature as a language, there should be a way to prevent this from happening or gracefully tackle this.</p>	1	< 0.1%
<p>I would like to know if there is an alternative to iFrames with HTML5. I mean by that, be able to inject cross-domains HTML inside of a webpage without using an iFrame.</p>	1	< 0.1%
<p>I am learning about Big O Notation running times and amortized times. I understand the notion of <em>O(n)</em> linear time, meaning that the size of the input affects the growth of the algorithm proportionally...and the same goes for, for example, quadratic time <em>O(n<sup>2</sup>)</em> etc..even algorithms, such as permutation generators, with <em>O(n!)</em> times, that grow by factorials.</p> <p>For example, the following function is <em>O(n)</em> because the algorithm grows in proportion to its input <em>n</em>:</p> <pre><code>f(int n) { int i; for (i = 0; i < n; ++i) printf("%d", i); } </code></pre> <p>Similarly, if there was a nested loop, the time would be O(n<sup>2</sup>).</p> <p>But what exactly is <em>O(log n)</em>? For example, what does it mean to say that the height of a complete binary tree is <em>O(log n)</em>?</p> <p>I do know (maybe not in great detail) what Logarithm is, in the sense that: log<sub>10</sub> 100 = 2, but I cannot understand how to identify a function with a logarithmic time.</p>	1	< 0.1%
<p>After I did brew update and brew upgrade, my postgres got some problem. I tried to uninstall postgres and install again, but it didn't work as well. </p> <p>This is the error message.(I also got this error message when I try to do rake db:migrate)</p> <pre><code>$ psql psql: could not connect to server: No such file or directory Is the server running locally and accepting connections on Unix domain socket "/tmp/.s.PGSQL.5432"? </code></pre> <p>How can I solve it?</p> <p>Mac version: Mountain lion.</p> <p>homebrew version: 0.9.3</p> <p>postgres version: psql (PostgreSQL) 9.2.1</p> <p>And this is what I did.</p> <pre><code>12:30 ~/D/works$ brew uninstall postgresql Uninstalling /usr/local/Cellar/postgresql/9.2.1... 12:31 ~/D/works$ brew uninstall postgresql Uninstalling /usr/local/Cellar/postgresql/9.1.4... 12:31 ~/D/works$ psql --version bash: /usr/local/bin/psql: No such file or directory 12:33 ~/D/works$ brew install postgresql ==> Downloading http://ftp.postgresql.org/pub/source/v9.2.1/postgresql-9.2.1.tar.bz2 Already downloaded: /Library/Caches/Homebrew/postgresql-9.2.1.tar.bz2 ...... ...... ==> Summary /usr/local/Cellar/postgresql/9.2.1: 2814 files, 38M, built in 2.7 minutes 12:37 ~/D/works$ initdb /usr/local/var/postgres -E utf8 The files belonging to this database system will be owned by user "laigary". This user must also own the server process. The database cluster will be initialized with locale "en_US.UTF-8". The default text search configuration will be set to "english". initdb: directory "/usr/local/var/postgres" exists but is not empty If you want to create a new database system, either remove or empty the directory "/usr/local/var/postgres" or run initdb with an argument other than "/usr/local/var/postgres". 12:39 ~/D/works$ mkdir -p ~/Library/LaunchAgents 12:39 ~/D/works$ cp /usr/local/Cellar/postgresql/9.2.1/homebrew.mxcl.postgresql.plist ~/Library/LaunchAgents/ 12:39 ~/D/works$ launchctl load -w ~/Library/LaunchAgents/homebrew.mxcl.postgresql.plist homebrew.mxcl.postgresql: Already loaded 12:39 ~/D/works$ pg_ctl -D /usr/local/var/postgres -l /usr/local/var/postgres/server.log start server starting 12:39 ~/D/works$ env ARCHFLAGS="-arch x86_64" gem install pg Building native extensions. This could take a while... Successfully installed pg-0.14.1 1 gem installed 12:42 ~/D/works$ psql --version psql (PostgreSQL) 9.2.1 12:42 ~/D/works$ psql psql: could not connect to server: No such file or directory Is the server running locally and accepting connections on Unix domain socket "/tmp/.s.PGSQL.5432"? </code></pre> <p>Now, after I reinstalled howbrew,when I use <code>$ psql</code>, It doesn't show any error message.</p> <p>But I run <code>rake db:migrate</code> in my rails app, it shows:</p> <pre><code>could not connect to server: No such file or directory Is the server running locally and accepting connections on Unix domain socket "/var/pgsql_socket/.s.PGSQL.5432"? /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/postgresql_adapter.rb:1213:in `initialize' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/postgresql_adapter.rb:1213:in `new' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/postgresql_adapter.rb:1213:in `connect' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/postgresql_adapter.rb:329:in `initialize' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/postgresql_adapter.rb:28:in `new' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/postgresql_adapter.rb:28:in `postgresql_connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:309:in `new_connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:319:in `checkout_new_connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:241:in `block (2 levels) in checkout' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:236:in `loop' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:236:in `block in checkout' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/monitor.rb:211:in `mon_synchronize' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:233:in `checkout' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:96:in `block in connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/monitor.rb:211:in `mon_synchronize' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:95:in `connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_pool.rb:404:in `retrieve_connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_specification.rb:170:in `retrieve_connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/connection_adapters/abstract/connection_specification.rb:144:in `connection' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/railties/databases.rake:107:in `rescue in create_database' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/railties/databases.rake:51:in `create_database' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/railties/databases.rake:40:in `block (3 levels) in <top (required)>' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/railties/databases.rake:40:in `each' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/gems/1.9.1/gems/activerecord-3.2.8/lib/active_record/railties/databases.rake:40:in `block (2 levels) in <top (required)>' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:205:in `call' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:205:in `block in execute' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:200:in `each' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:200:in `execute' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:158:in `block in invoke_with_call_chain' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/monitor.rb:211:in `mon_synchronize' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:151:in `invoke_with_call_chain' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/task.rb:144:in `invoke' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:116:in `invoke_task' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:94:in `block (2 levels) in top_level' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:94:in `each' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:94:in `block in top_level' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:133:in `standard_exception_handling' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:88:in `top_level' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:66:in `block in run' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:133:in `standard_exception_handling' /usr/local/Cellar/ruby/1.9.3-p327/lib/ruby/1.9.1/rake/application.rb:63:in `run' /usr/local/bin/rake:32:in `<main>' Couldn't create database for {"adapter"=>"postgresql", "encoding"=>"unicode", "database"=>"riy_development", "pool"=>5, "username"=>nil, "password"=>nil} </code></pre> <p><strong>Finally I find the solution.</strong></p> <pre><code>$ sudo mkdir /var/pgsql_socket/ $ sudo ln -s /private/tmp/.s.PGSQL.5432 /var/pgsql_socket/ </code></pre> <p>This solution is little tricky, but it works. Hope anyone have a better solution</p> <p><strong>Update</strong></p> <p>This works for me as well.</p> <pre><code>rm /usr/local/var/postgres/postmaster.pid </code></pre>	1	< 0.1%
Other values (11925)	11925	99.9%

Histogram of lengths of the category

Value	Count	Frequency (%)
the	49308	3.9%
	40181	3.2%
to	36545	2.9%
a	33430	2.7%
i	26570	2.1%
and	19267	1.5%
is	18081	1.4%
in	16547	1.3%
of	16077	1.3%
that	11930	0.9%
Other values (116595)	992056	78.7%

Most occurring characters

Value	Count	Frequency (%)
	1407644	14.2%
e	809309	8.2%
t	620460	6.3%
o	538281	5.4%
a	467639	4.7%
i	433918	4.4%
n	420878	4.3%
r	414914	4.2%
s	389979	3.9%
l	288910	2.9%
Other values (398)	4103829	41.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6546881	66.2%
Space Separator	1408105	14.2%
Other Punctuation	589974	6.0%
Math Symbol	472711	4.8%
Uppercase Letter	358863	3.6%
Control	184345	1.9%
Decimal Number	152567	1.5%
Close Punctuation	52665	0.5%
Open Punctuation	52594	0.5%
Dash Punctuation	50969	0.5%
Other values (12)	26087	0.3%

Most frequent character per category

Value	Count	Frequency (%)
͗	6	2.3%
̮	6	2.3%
͋	6	2.3%
͇	6	2.3%
̤	5	1.9%
̀	5	1.9%
ͤ	5	1.9%
̗	5	1.9%
̱	5	1.9%
̊	5	1.9%
Other values (89)	210	79.5%

Value	Count	Frequency (%)
e	809309	12.4%
t	620460	9.5%
o	538281	8.2%
a	467639	7.1%
i	433918	6.6%
n	420878	6.4%
r	414914	6.3%
s	389979	6.0%
l	288910	4.4%
d	275514	4.2%
Other values (80)	1887079	28.8%

Value	Count	Frequency (%)
I	64134	17.9%
S	29306	8.2%
T	25311	7.1%
A	21882	6.1%
C	21545	6.0%
E	16948	4.7%
P	15871	4.4%
L	14334	4.0%
R	13847	3.9%
M	13683	3.8%
Other values (36)	122002	34.0%

Value	Count	Frequency (%)
没	6	4.9%
有	6	4.9%
那	6	4.9%
个	6	4.9%
文	6	4.9%
件	6	4.9%
或	6	4.9%
目	6	4.9%
录	6	4.9%
ಠ	6	4.9%
Other values (30)	63	51.2%

Value	Count	Frequency (%)
─	261	34.6%
═	120	15.9%
├	78	10.3%
│	56	7.4%
└	55	7.3%
¦	48	6.4%
║	24	3.2%
👩	20	2.6%
☺	12	1.6%
👧	10	1.3%
Other values (27)	71	9.4%

Value	Count	Frequency (%)
/	178398	30.2%
.	112134	19.0%
"	59611	10.1%
,	52014	8.8%
;	44558	7.6%
:	44120	7.5%
'	33220	5.6%
&	27253	4.6%
?	19027	3.2%
#	5115	0.9%
Other values (14)	14524	2.5%

Value	Count	Frequency (%)
>	213954	45.3%
<	213663	45.2%
=	36502	7.7%
+	5854	1.2%
\|	2257	0.5%
~	433	0.1%
→	21	< 0.1%
×	13	< 0.1%
←	3	< 0.1%
∞	2	< 0.1%
Other values (7)	9	< 0.1%

Value	Count	Frequency (%)
0	30561	20.0%
1	27061	17.7%
2	22106	14.5%
3	14376	9.4%
4	11854	7.8%
5	10855	7.1%
6	9768	6.4%
9	9070	5.9%
8	8936	5.9%
7	7980	5.2%

Value	Count	Frequency (%)
	1407644	> 99.9%
	371	< 0.1%
	28	< 0.1%
	20	< 0.1%
	20	< 0.1%
	20	< 0.1%
	2	< 0.1%

Value	Count	Frequency (%)
(	37405	71.1%
{	8678	16.5%
[	6509	12.4%
„	1	< 0.1%
‚	1	< 0.1%

Value	Count	Frequency (%)
`	963	74.0%
^	331	25.4%
´	7	0.5%
˜	1	0.1%

Value	Count	Frequency (%)
$	3797	99.7%
£	5	0.1%
¢	4	0.1%
¤	1	< 0.1%

Value	Count	Frequency (%)
²	6	60.0%
³	2	20.0%
⁴	1	10.0%
¹	1	10.0%

Value	Count	Frequency (%)
-	50818	99.7%
–	82	0.2%
—	69	0.1%

Value	Count	Frequency (%)
)	37582	71.4%
}	8623	16.4%
]	6460	12.3%

Value	Count	Frequency (%)
	182080	98.8%
	2238	1.2%
	27	< 0.1%

Value	Count	Frequency (%)
“	124	71.3%
‘	48	27.6%
«	2	1.1%

Value	Count	Frequency (%)
’	296	68.4%
”	135	31.2%
»	2	0.5%

Value	Count	Frequency (%)
	57	68.7%
‍	25	30.1%
‏	1	1.2%

Value	Count	Frequency (%)
_	19132	100.0%

Value	Count	Frequency (%)
҉	3	100.0%

Value	Count	Frequency (%)
	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6905600	69.8%
Common	2989609	30.2%
Inherited	289	< 0.1%
Cyrillic	124	< 0.1%
Han	65	< 0.1%
Hangul	39	< 0.1%
Greek	16	< 0.1%
Hebrew	7	< 0.1%
Kannada	6	< 0.1%
Hiragana	4	< 0.1%

Most frequent character per script

Value	Count	Frequency (%)
	1407644	47.1%
>	213954	7.2%
<	213663	7.1%
	182080	6.1%
/	178398	6.0%
.	112134	3.8%
"	59611	2.0%
,	52014	1.7%
-	50818	1.7%
;	44558	1.5%
Other values (122)	474735	15.9%

Value	Count	Frequency (%)
‍	25	8.7%
͗	6	2.1%
̮	6	2.1%
͋	6	2.1%
͇	6	2.1%
̤	5	1.7%
̀	5	1.7%
ͤ	5	1.7%
̗	5	1.7%
̱	5	1.7%
Other values (90)	215	74.4%

Value	Count	Frequency (%)
e	809309	11.7%
t	620460	9.0%
o	538281	7.8%
a	467639	6.8%
i	433918	6.3%
n	420878	6.1%
r	414914	6.0%
s	389979	5.6%
l	288910	4.2%
d	275514	4.0%
Other values (86)	2245798	32.5%

Value	Count	Frequency (%)
о	17	13.7%
а	12	9.7%
к	10	8.1%
н	10	8.1%
е	9	7.3%
т	8	6.5%
и	7	5.6%
с	5	4.0%
Г	4	3.2%
р	4	3.2%
Other values (22)	38	30.6%

Value	Count	Frequency (%)
没	6	9.2%
有	6	9.2%
那	6	9.2%
个	6	9.2%
文	6	9.2%
件	6	9.2%
或	6	9.2%
目	6	9.2%
录	6	9.2%
草	3	4.6%
Other values (4)	8	12.3%

Value	Count	Frequency (%)
지	3	7.7%
정	3	7.7%
된	3	7.7%
경	3	7.7%
로	3	7.7%
를	3	7.7%
찾	3	7.7%
을	3	7.7%
수	3	7.7%
없	3	7.7%
Other values (3)	9	23.1%

Value	Count	Frequency (%)
Θ	3	18.8%
π	2	12.5%
ε	2	12.5%
ί	2	12.5%
ν	2	12.5%
α	2	12.5%
ι	2	12.5%
Ω	1	6.2%

Value	Count	Frequency (%)
ב	1	14.3%
ר	1	14.3%
י	1	14.3%
צ	1	14.3%
ק	1	14.3%
ל	1	14.3%
ה	1	14.3%

Value	Count	Frequency (%)
お	1	25.0%
は	1	25.0%
よ	1	25.0%
う	1	25.0%

Value	Count	Frequency (%)
ಠ	6	100.0%

Value	Count	Frequency (%)
ܫ	2	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9892819	> 99.9%
Punctuation	1018	< 0.1%
None	700	< 0.1%
Box Drawing	607	< 0.1%
Diacriticals	264	< 0.1%
Cyrillic	124	< 0.1%
CJK	65	< 0.1%
Hangul	39	< 0.1%
Arrows	30	< 0.1%
Misc Symbols	25	< 0.1%
Other values (13)	70	< 0.1%

Most frequent character per block

Value	Count	Frequency (%)
	1407644	14.2%
e	809309	8.2%
t	620460	6.3%
o	538281	5.4%
a	467639	4.7%
i	433918	4.4%
n	420878	4.3%
r	414914	4.2%
s	389979	3.9%
l	288910	2.9%
Other values (88)	4100887	41.5%

Value	Count	Frequency (%)
’	296	29.1%
”	135	13.3%
“	124	12.2%
–	82	8.1%
…	70	6.9%
—	69	6.8%
	57	5.6%
‘	48	4.7%
	28	2.8%
‍	25	2.5%
Other values (10)	84	8.3%

Value	Count	Frequency (%)
	371	53.0%
¦	48	6.9%
ö	21	3.0%
👩	20	2.9%
ä	19	2.7%
å	18	2.6%
×	13	1.9%
é	13	1.9%
👧	10	1.4%
👦	10	1.4%
Other values (68)	157	22.4%

Value	Count	Frequency (%)
➔	2	40.0%
➜	2	40.0%
❤	1	20.0%

Value	Count	Frequency (%)
─	261	43.0%
═	120	19.8%
├	78	12.9%
│	56	9.2%
└	55	9.1%
║	24	4.0%
┬	5	0.8%
╔	2	0.3%
╗	2	0.3%
╚	2	0.3%

Value	Count	Frequency (%)
没	6	9.2%
有	6	9.2%
那	6	9.2%
个	6	9.2%
文	6	9.2%
件	6	9.2%
或	6	9.2%
目	6	9.2%
录	6	9.2%
草	3	4.6%
Other values (4)	8	12.3%

Value	Count	Frequency (%)
🟡	1	50.0%
🟢	1	50.0%

Value	Count	Frequency (%)
⌘	9	100.0%

Value	Count	Frequency (%)
→	21	70.0%
←	3	10.0%
⇧	2	6.7%
↔	2	6.7%
↑	1	3.3%
↹	1	3.3%

Value	Count	Frequency (%)
™	2	100.0%

Value	Count	Frequency (%)
͗	6	2.3%
̮	6	2.3%
͋	6	2.3%
͇	6	2.3%
̤	5	1.9%
̀	5	1.9%
ͤ	5	1.9%
̗	5	1.9%
̱	5	1.9%
̊	5	1.9%
Other values (89)	210	79.5%

Value	Count	Frequency (%)
о	17	13.7%
а	12	9.7%
к	10	8.1%
н	10	8.1%
е	9	7.3%
т	8	6.5%
и	7	5.6%
с	5	4.0%
Г	4	3.2%
р	4	3.2%
Other values (22)	38	30.6%

Value	Count	Frequency (%)
∞	2	25.0%
∃	2	25.0%
√	1	12.5%
∀	1	12.5%
∉	1	12.5%
⊢	1	12.5%

Value	Count	Frequency (%)
�	3	100.0%

Value	Count	Frequency (%)
지	3	7.7%
정	3	7.7%
된	3	7.7%
경	3	7.7%
로	3	7.7%
를	3	7.7%
찾	3	7.7%
을	3	7.7%
수	3	7.7%
없	3	7.7%
Other values (3)	9	23.1%

Value	Count	Frequency (%)
☺	12	48.0%
♨	4	16.0%
♠	2	8.0%
♥	2	8.0%
♦	2	8.0%
♣	2	8.0%
☁	1	4.0%

Value	Count	Frequency (%)
ಠ	6	100.0%

Value	Count	Frequency (%)
◎	4	100.0%

Value	Count	Frequency (%)
ܫ	2	100.0%

Value	Count	Frequency (%)
˜	1	100.0%

Value	Count	Frequency (%)
お	1	25.0%
は	1	25.0%
よ	1	25.0%
う	1	25.0%

Value	Count	Frequency (%)
ב	1	14.3%
ר	1	14.3%
י	1	14.3%
צ	1	14.3%
ק	1	14.3%
ל	1	14.3%
ה	1	14.3%

Value	Count	Frequency (%)
﹕	17	100.0%

OwnerUserId
Real number (ℝ_≥0)

MISSING

Distinct	9365
Distinct (%)	80.5%
Missing	306
Missing (%)	2.6%
Infinite	0
Infinite (%)	0.0%

Mean	629360.9859
Minimum	1
Maximum	11407978
Zeros	0
Zeros (%)	0.0%
Memory size	93.4 KiB

Quantile statistics

Minimum	1
5-th percentile	2187.4
Q1	49887
median	234322
Q3	759568
95-th percentile	2629844.2
Maximum	11407978
Range	11407977
Interquartile range (IQR)	709681

Descriptive statistics

Standard deviation	1039230.318
Coefficient of variation (CV)	1.651246807
Kurtosis	17.64724265
Mean	629360.9859
Median Absolute Deviation (MAD)	218174
Skewness	3.53420404
Sum	7318838905
Variance	1.079999654 × 10¹²
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
63051	18	0.2%
49153	15	0.1%
179736	14	0.1%
39677	13	0.1%
4872	13	0.1%
9951	13	0.1%
4883	12	0.1%
1384652	12	0.1%
143378	12	0.1%
15055	11	0.1%
Other values (9355)	11496	96.3%
(Missing)	306	2.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	6	0.1%
3	1	< 0.1%
4	2	< 0.1%
5	1	< 0.1%
9	1	< 0.1%

Value	Count	Frequency (%)
11407978	1	< 0.1%
11072841	1	< 0.1%
10892691	1	< 0.1%
10339589	1	< 0.1%
10133797	1	< 0.1%

OwnerDisplayName
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct	1713
Distinct (%)	71.5%
Missing	9539
Missing (%)	79.9%
Memory size	93.4 KiB

Ray Vega	12
Dan	11
Tom	10
Tim	10
anon	9
Other values (1708)	2344

Length

Max length	25
Median length	8
Mean length	8.379799666
Min length	1

Characters and Unicode

Total characters	20078
Distinct characters	80
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	3 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1352 ?
Unique (%)	56.4%

Sample

1st row	John Sheehan
2nd row	swilliams
3rd row	Oded
4th row	Matt Sheppard
5th row	Patrick McElhaney

Value	Count	Frequency (%)
Ray Vega	12	0.1%
Dan	11	0.1%
Tom	10	0.1%
Tim	10	0.1%
anon	9	0.1%
J. Pablo Fernández	9	0.1%
Claudiu	9	0.1%
Simucal	9	0.1%
Matt	9	0.1%
Bill the Lizard	8	0.1%
Other values (1703)	2300	19.3%
(Missing)	9539	79.9%

Histogram of lengths of the category

Value	Count	Frequency (%)
matt	27	0.9%
brian	24	0.8%
john	22	0.7%
mark	20	0.6%
chris	19	0.6%
adam	18	0.6%
dan	18	0.6%
j	16	0.5%
david	16	0.5%
paul	15	0.5%
Other values (1871)	2977	93.9%

Most occurring characters

Value	Count	Frequency (%)
a	1711	8.5%
e	1696	8.4%
r	1313	6.5%
n	1261	6.3%
o	1192	5.9%
i	1134	5.6%
s	847	4.2%
	780	3.9%
l	779	3.9%
t	764	3.8%
Other values (70)	8601	42.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	15602	77.7%
Uppercase Letter	2647	13.2%
Decimal Number	892	4.4%
Space Separator	780	3.9%
Other Punctuation	124	0.6%
Dash Punctuation	20	0.1%
Connector Punctuation	13	0.1%

Most frequent character per category

Value	Count	Frequency (%)
a	1711	11.0%
e	1696	10.9%
r	1313	8.4%
n	1261	8.1%
o	1192	7.6%
i	1134	7.3%
s	847	5.4%
l	779	5.0%
t	764	4.9%
u	550	3.5%
Other values (26)	4355	27.9%

Value	Count	Frequency (%)
S	243	9.2%
M	240	9.1%
J	202	7.6%
B	186	7.0%
D	177	6.7%
A	174	6.6%
C	171	6.5%
G	135	5.1%
T	133	5.0%
R	126	4.8%
Other values (17)	860	32.5%

Value	Count	Frequency (%)
2	144	16.1%
1	118	13.2%
3	105	11.8%
0	98	11.0%
4	89	10.0%
9	73	8.2%
8	70	7.8%
5	69	7.7%
6	68	7.6%
7	58	6.5%

Value	Count	Frequency (%)
.	61	49.2%
&	21	16.9%
#	21	16.9%
;	21	16.9%

Value	Count	Frequency (%)
	780	100.0%

Value	Count	Frequency (%)
_	13	100.0%

Value	Count	Frequency (%)
-	20	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	18249	90.9%
Common	1829	9.1%

Most frequent character per script

Value	Count	Frequency (%)
a	1711	9.4%
e	1696	9.3%
r	1313	7.2%
n	1261	6.9%
o	1192	6.5%
i	1134	6.2%
s	847	4.6%
l	779	4.3%
t	764	4.2%
u	550	3.0%
Other values (53)	7002	38.4%

Value	Count	Frequency (%)
	780	42.6%
2	144	7.9%
1	118	6.5%
3	105	5.7%
0	98	5.4%
4	89	4.9%
9	73	4.0%
8	70	3.8%
5	69	3.8%
6	68	3.7%
Other values (7)	215	11.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	20065	99.9%
None	8	< 0.1%
IPA Ext	5	< 0.1%

Most frequent character per block

Value	Count	Frequency (%)
a	1711	8.5%
e	1696	8.5%
r	1313	6.5%
n	1261	6.3%
o	1192	5.9%
i	1134	5.7%
s	847	4.2%
	780	3.9%
l	779	3.9%
t	764	3.8%
Other values (59)	8588	42.8%

Value	Count	Frequency (%)
Š	2	25.0%
ǝ	2	25.0%
š	1	12.5%
ź	1	12.5%
ń	1	12.5%
ı	1	12.5%

Value	Count	Frequency (%)
ɐ	1	20.0%
ɯ	1	20.0%
ʃ	1	20.0%
ʞ	1	20.0%
ɔ	1	20.0%

LastEditorUserId
Real number (ℝ)

MISSING

Distinct	4744
Distinct (%)	42.5%
Missing	776
Missing (%)	6.5%
Infinite	0
Infinite (%)	0.0%

Mean	2053495.456
Minimum	-1
Maximum	15054795
Zeros	0
Zeros (%)	0.0%
Memory size	93.4 KiB

Quantile statistics

Minimum	-1
5-th percentile	-1
Q1	135729.5
median	875915
Q3	3064538
95-th percentile	7906188
Maximum	15054795
Range	15054796
Interquartile range (IQR)	2928808.5

Descriptive statistics

Standard deviation	2685047.38
Coefficient of variation (CV)	1.307549706
Kurtosis	3.209228415
Mean	2053495.456
Median Absolute Deviation (MAD)	820128
Skewness	1.825662309
Sum	2.29149558 × 10¹⁰
Variance	7.209479434 × 10¹²
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
63550	755	6.3%
-1	591	5.0%
6862601	107	0.9%
1033581	106	0.9%
355230	104	0.9%
860099	90	0.8%
806202	89	0.7%
3924118	74	0.6%
2756409	59	0.5%
3885376	54	0.5%
Other values (4734)	9130	76.5%
(Missing)	776	6.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-1	591	5.0%
1	1	< 0.1%
3	1	< 0.1%
23	1	< 0.1%
29	1	< 0.1%

Value	Count	Frequency (%)
15054795	2	< 0.1%
14979760	1	< 0.1%
14868997	1	< 0.1%
14837094	1	< 0.1%
14638396	1	< 0.1%

LastEditorDisplayName
Categorical

HIGH CARDINALITY
MISSING

Distinct	907
Distinct (%)	52.9%
Missing	10220
Missing (%)	85.6%
Memory size	93.4 KiB

user456814	75
Roger Pate	29
user212218	25
Rich B	22
user166390	20
Other values (902)	1544

Length

Max length	25
Median length	10
Mean length	9.232069971
Min length	2

Characters and Unicode

Total characters	15833
Distinct characters	82
Distinct categories	7 ?
Distinct scripts	3 ?
Distinct blocks	3 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	650 ?
Unique (%)	37.9%

Sample

1st row	John Sheehan
2nd row	Jakub Šturc
3rd row	ΤΖΩΤΖΙΟΥ
4th row	Chris Fournier
5th row	cynicalman

Value	Count	Frequency (%)
user456814	75	0.6%
Roger Pate	29	0.2%
user212218	25	0.2%
Rich B	22	0.2%
user166390	20	0.2%
J.F. Sebastian	17	0.1%
Chris Hanson	17	0.1%
user6269864	13	0.1%
Ned Batchelder	12	0.1%
user1228	12	0.1%
Other values (897)	1473	12.3%
(Missing)	10220	85.6%

Histogram of lengths of the category

Value	Count	Frequency (%)
user456814	75	3.1%
chris	43	1.8%
b	30	1.2%
roger	29	1.2%
pate	29	1.2%
user212218	25	1.0%
brian	24	1.0%
rich	23	1.0%
john	22	0.9%
mark	21	0.9%
Other values (1062)	2093	86.7%

Most occurring characters

Value	Count	Frequency (%)
e	1353	8.5%
r	1133	7.2%
a	1057	6.7%
s	803	5.1%
n	801	5.1%
o	783	4.9%
i	734	4.6%
	703	4.4%
u	556	3.5%
t	515	3.3%
Other values (72)	7395	46.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	10953	69.2%
Decimal Number	2191	13.8%
Uppercase Letter	1882	11.9%
Space Separator	703	4.4%
Other Punctuation	99	0.6%
Dash Punctuation	3	< 0.1%
Connector Punctuation	2	< 0.1%

Most frequent character per category

Value	Count	Frequency (%)
B	180	9.6%
J	156	8.3%
M	154	8.2%
R	143	7.6%
S	142	7.5%
C	139	7.4%
A	110	5.8%
D	108	5.7%
G	84	4.5%
P	84	4.5%
Other values (23)	582	30.9%

Value	Count	Frequency (%)
e	1353	12.4%
r	1133	10.3%
a	1057	9.7%
s	803	7.3%
n	801	7.3%
o	783	7.1%
i	734	6.7%
u	556	5.1%
t	515	4.7%
l	501	4.6%
Other values (22)	2717	24.8%

Value	Count	Frequency (%)
1	319	14.6%
6	303	13.8%
4	278	12.7%
8	255	11.6%
5	251	11.5%
2	222	10.1%
3	174	7.9%
9	167	7.6%
7	118	5.4%
0	104	4.7%

Value	Count	Frequency (%)
.	69	69.7%
&	10	10.1%
#	10	10.1%
;	10	10.1%

Value	Count	Frequency (%)
	703	100.0%

Value	Count	Frequency (%)
-	3	100.0%

Value	Count	Frequency (%)
_	2	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	12795	80.8%
Common	2998	18.9%
Greek	40	0.3%

Most frequent character per script

Value	Count	Frequency (%)
e	1353	10.6%
r	1133	8.9%
a	1057	8.3%
s	803	6.3%
n	801	6.3%
o	783	6.1%
i	734	5.7%
u	556	4.3%
t	515	4.0%
l	501	3.9%
Other values (49)	4559	35.6%

Value	Count	Frequency (%)
	703	23.4%
1	319	10.6%
6	303	10.1%
4	278	9.3%
8	255	8.5%
5	251	8.4%
2	222	7.4%
3	174	5.8%
9	167	5.6%
7	118	3.9%
Other values (7)	208	6.9%

Value	Count	Frequency (%)
Τ	10	25.0%
Ζ	10	25.0%
Ω	5	12.5%
Ι	5	12.5%
Ο	5	12.5%
Υ	5	12.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	15784	99.7%
None	47	0.3%
IPA Ext	2	< 0.1%

Most frequent character per block

Value	Count	Frequency (%)
e	1353	8.6%
r	1133	7.2%
a	1057	6.7%
s	803	5.1%
n	801	5.1%
o	783	5.0%
i	734	4.7%
	703	4.5%
u	556	3.5%
t	515	3.3%
Other values (59)	7346	46.5%

Value	Count	Frequency (%)
Τ	10	21.3%
Ζ	10	21.3%
Ω	5	10.6%
Ι	5	10.6%
Ο	5	10.6%
Υ	5	10.6%
Š	3	6.4%
ź	1	2.1%
ń	1	2.1%
ć	1	2.1%

Value	Count	Frequency (%)
ʞ	1	50.0%
ɔ	1	50.0%

LastEditDate
Categorical

HIGH CARDINALITY
MISSING

Distinct	10984
Distinct (%)	97.1%
Missing	620
Missing (%)	5.2%
Memory size	93.4 KiB

2020-06-20 09:12:55	106
2017-05-23 12:10:41	9
2017-05-23 12:34:45	8
2017-05-23 12:26:23	7
2017-05-23 12:18:22	7
Other values (10979)	11178

Length

Max length	19
Median length	19
Mean length	19
Min length	19

Characters and Unicode

Total characters	214985
Distinct characters	13
Distinct categories	4 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	10871 ?
Unique (%)	96.1%

Sample

1st row	2020-05-06 08:43:38
2nd row	2013-12-11 17:09:20
3rd row	2020-02-22 17:25:33
4th row	2018-03-26 08:14:43
5th row	2016-09-16 04:18:58

Value	Count	Frequency (%)
2020-06-20 09:12:55	106	0.9%
2017-05-23 12:10:41	9	0.1%
2017-05-23 12:34:45	8	0.1%
2017-05-23 12:26:23	7	0.1%
2017-05-23 12:18:22	7	0.1%
2017-05-23 12:26:35	6	0.1%
2017-05-23 11:55:10	6	0.1%
2017-05-23 11:47:28	6	0.1%
2017-05-23 10:31:38	5	< 0.1%
2017-05-23 11:33:26	5	< 0.1%
Other values (10974)	11150	93.4%
(Missing)	620	5.2%

Histogram of lengths of the category

Value	Count	Frequency (%)
2017-05-23	437	1.9%
2020-06-20	108	0.5%
09:12:55	106	0.5%
2020-04-19	48	0.2%
2020-04-17	38	0.2%
2017-03-22	21	0.1%
2021-04-01	18	0.1%
2019-02-27	16	0.1%
2017-09-10	15	0.1%
2018-11-02	14	0.1%
Other values (13738)	21809	96.4%

Most occurring characters

Value	Count	Frequency (%)
0	37934	17.6%
1	32772	15.2%
2	30177	14.0%
-	22630	10.5%
:	22630	10.5%
	11315	5.3%
3	10761	5.0%
5	10299	4.8%
4	9740	4.5%
9	7131	3.3%
Other values (3)	19596	9.1%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	158410	73.7%
Dash Punctuation	22630	10.5%
Other Punctuation	22630	10.5%
Space Separator	11315	5.3%

Most frequent character per category

Value	Count	Frequency (%)
0	37934	23.9%
1	32772	20.7%
2	30177	19.0%
3	10761	6.8%
5	10299	6.5%
4	9740	6.1%
9	7131	4.5%
7	6913	4.4%
8	6639	4.2%
6	6044	3.8%

Value	Count	Frequency (%)
-	22630	100.0%

Value	Count	Frequency (%)
	11315	100.0%

Value	Count	Frequency (%)
:	22630	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	214985	100.0%

Most frequent character per script

Value	Count	Frequency (%)
0	37934	17.6%
1	32772	15.2%
2	30177	14.0%
-	22630	10.5%
:	22630	10.5%
	11315	5.3%
3	10761	5.0%
5	10299	4.8%
4	9740	4.5%
9	7131	3.3%
Other values (3)	19596	9.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	214985	100.0%

Most frequent character per block

Value	Count	Frequency (%)
0	37934	17.6%
1	32772	15.2%
2	30177	14.0%
-	22630	10.5%
:	22630	10.5%
	11315	5.3%
3	10761	5.0%
5	10299	4.8%
4	9740	4.5%
9	7131	3.3%
Other values (3)	19596	9.1%

LastActivityDate
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	11933
Distinct (%)	> 99.9%
Missing	0
Missing (%)	0.0%
Memory size	93.4 KiB

2020-01-31 06:45:01	2
2020-01-31 06:44:36	2
2020-02-13 17:07:39	1
2020-10-06 02:01:45	1
2021-03-24 06:23:06	1
Other values (11928)	11928

Length

Max length	19
Median length	19
Mean length	19
Min length	19

Characters and Unicode

Total characters	226765
Distinct characters	13
Distinct categories	4 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	11931 ?
Unique (%)	> 99.9%

Sample

1st row	2020-09-27 11:39:24
2nd row	2018-09-12 07:43:01
3rd row	2020-12-16 18:32:49
4th row	2021-01-14 02:35:41
5th row	2021-02-28 21:59:41

Value	Count	Frequency (%)
2020-01-31 06:45:01	2	< 0.1%
2020-01-31 06:44:36	2	< 0.1%
2020-02-13 17:07:39	1	< 0.1%
2020-10-06 02:01:45	1	< 0.1%
2021-03-24 06:23:06	1	< 0.1%
2020-03-29 11:57:16	1	< 0.1%
2021-04-06 20:57:32	1	< 0.1%
2021-01-20 23:39:58	1	< 0.1%
2020-05-29 04:56:28	1	< 0.1%
2020-11-24 06:44:16	1	< 0.1%
Other values (11923)	11923	99.9%

Histogram of lengths of the category

Value	Count	Frequency (%)
2021-04-07	84	0.4%
2021-04-08	81	0.3%
2021-04-06	71	0.3%
2021-04-09	66	0.3%
2021-03-24	65	0.3%
2021-03-25	64	0.3%
2021-03-31	63	0.3%
2021-03-23	60	0.3%
2021-04-02	59	0.2%
2021-04-01	59	0.2%
Other values (13041)	23198	97.2%

Most occurring characters

Value	Count	Frequency (%)
0	43300	19.1%
2	37278	16.4%
1	31616	13.9%
-	23870	10.5%
:	23870	10.5%
	11935	5.3%
3	11326	5.0%
4	9793	4.3%
5	9284	4.1%
9	6990	3.1%
Other values (3)	17503	7.7%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	167090	73.7%
Dash Punctuation	23870	10.5%
Other Punctuation	23870	10.5%
Space Separator	11935	5.3%

Most frequent character per category

Value	Count	Frequency (%)
0	43300	25.9%
2	37278	22.3%
1	31616	18.9%
3	11326	6.8%
4	9793	5.9%
5	9284	5.6%
9	6990	4.2%
8	6186	3.7%
7	5783	3.5%
6	5534	3.3%

Value	Count	Frequency (%)
-	23870	100.0%

Value	Count	Frequency (%)
	11935	100.0%

Value	Count	Frequency (%)
:	23870	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	226765	100.0%

Most frequent character per script

Value	Count	Frequency (%)
0	43300	19.1%
2	37278	16.4%
1	31616	13.9%
-	23870	10.5%
:	23870	10.5%
	11935	5.3%
3	11326	5.0%
4	9793	4.3%
5	9284	4.1%
9	6990	3.1%
Other values (3)	17503	7.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	226765	100.0%

Most frequent character per block

Value	Count	Frequency (%)
0	43300	19.1%
2	37278	16.4%
1	31616	13.9%
-	23870	10.5%
:	23870	10.5%
	11935	5.3%
3	11326	5.0%
4	9793	4.3%
5	9284	4.1%
9	6990	3.1%
Other values (3)	17503	7.7%

Title
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct	11935
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Memory size	93.4 KiB

How can I use Ruby to colorize the text output to a terminal?	1
How do I redirect in expressjs while passing some context?	1
Get MD5 hash of big files in Python	1
How to Free Inode Usage?	1
How can I stop .gitignore from appearing in the list of untracked files?	1
Other values (11930)	11930

Length

Max length	149
Median length	48
Mean length	50.38868873
Min length	13

Characters and Unicode

Total characters	601389
Distinct characters	116
Distinct categories	17 ?
Distinct scripts	6 ?
Distinct blocks	8 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	11935 ?
Unique (%)	100.0%

Sample

1st row	Dynamic LINQ OrderBy on IEnumerable<T> / IQueryable<T>
2nd row	Prevent RequireJS from Caching Required Scripts
3rd row	How do you rotate a two dimensional array?
4th row	Best way to get identity of inserted row?
5th row	How do you synchronise projects to GitHub with Android Studio?

Value	Count	Frequency (%)
How can I use Ruby to colorize the text output to a terminal?	1	< 0.1%
How do I redirect in expressjs while passing some context?	1	< 0.1%
Get MD5 hash of big files in Python	1	< 0.1%
How to Free Inode Usage?	1	< 0.1%
How can I stop .gitignore from appearing in the list of untracked files?	1	< 0.1%
Call a "local" function within module.exports from another function in module.exports?	1	< 0.1%
Get String in YYYYMMDD format from JS date object?	1	< 0.1%
jQuery Ajax calls and the Html.AntiForgeryToken()	1	< 0.1%
Add Bootstrap Glyphicon to Input Box	1	< 0.1%
How to remove focus border (outline) around text/input boxes? (Chrome)	1	< 0.1%
Other values (11925)	11925	99.9%

Histogram of lengths of the category

Value	Count	Frequency (%)
to	4252	4.2%
how	3728	3.7%
in	3637	3.6%
a	3626	3.6%
the	2507	2.5%
and	1798	1.8%
is	1568	1.5%
i	1451	1.4%
of	1366	1.3%
what	1335	1.3%
Other values (8901)	76489	75.2%

Most occurring characters

Value	Count	Frequency (%)
	89829	14.9%
e	52406	8.7%
t	41400	6.9%
o	38077	6.3%
a	36970	6.1%
n	35151	5.8%
i	34919	5.8%
r	29138	4.8%
s	26213	4.4%
l	17461	2.9%
Other values (106)	199825	33.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	448000	74.5%
Space Separator	89829	14.9%
Uppercase Letter	41238	6.9%
Other Punctuation	14451	2.4%
Decimal Number	2072	0.3%
Dash Punctuation	1532	0.3%
Open Punctuation	1197	0.2%
Close Punctuation	1192	0.2%
Math Symbol	1154	0.2%
Connector Punctuation	435	0.1%
Other values (7)	289	< 0.1%

Most frequent character per category

Value	Count	Frequency (%)
H	4495	10.9%
S	4314	10.5%
I	3177	7.7%
C	2706	6.6%
W	2549	6.2%
P	2541	6.2%
A	2171	5.3%
T	1842	4.5%
L	1657	4.0%
D	1577	3.8%
Other values (18)	14209	34.5%

Value	Count	Frequency (%)
e	52406	11.7%
t	41400	9.2%
o	38077	8.5%
a	36970	8.3%
n	35151	7.8%
i	34919	7.8%
r	29138	6.5%
s	26213	5.9%
l	17461	3.9%
c	17241	3.8%
Other values (17)	119024	26.6%

Value	Count	Frequency (%)
?	6417	44.4%
.	1784	12.3%
"	1369	9.5%
'	1303	9.0%
,	1140	7.9%
:	943	6.5%
/	869	6.0%
#	201	1.4%
*	106	0.7%
@	93	0.6%
Other values (7)	226	1.6%

Value	Count	Frequency (%)
0	409	19.7%
1	355	17.1%
2	299	14.4%
3	208	10.0%
4	196	9.5%
8	157	7.6%
6	154	7.4%
5	142	6.9%
7	98	4.7%
9	54	2.6%

Value	Count	Frequency (%)
+	601	52.1%
>	186	16.1%
<	173	15.0%
=	170	14.7%
~	12	1.0%
\|	11	1.0%
→	1	0.1%

Value	Count	Frequency (%)
◎	2	25.0%
👩	2	25.0%
►	1	12.5%
☺	1	12.5%
👧	1	12.5%
👦	1	12.5%

Value	Count	Frequency (%)
-	1511	98.6%
—	11	0.7%
–	10	0.7%

Value	Count	Frequency (%)
(	1098	91.7%
[	78	6.5%
{	21	1.8%

Value	Count	Frequency (%)
)	1097	92.0%
]	74	6.2%
}	21	1.8%

Value	Count	Frequency (%)
`	133	92.4%
^	11	7.6%

Value	Count	Frequency (%)
“	15	75.0%
‘	5	25.0%

Value	Count	Frequency (%)
’	17	51.5%
”	16	48.5%

Value	Count	Frequency (%)
‍	3	75.0%
	1	25.0%

Value	Count	Frequency (%)
	89829	100.0%

Value	Count	Frequency (%)
$	79	100.0%

Value	Count	Frequency (%)
_	435	100.0%

Value	Count	Frequency (%)
ܫ	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	489234	81.4%
Common	112147	18.6%
Greek	3	< 0.1%
Inherited	3	< 0.1%
Syriac	1	< 0.1%
Cyrillic	1	< 0.1%

Most frequent character per script

Value	Count	Frequency (%)
	89829	80.1%
?	6417	5.7%
.	1784	1.6%
-	1511	1.3%
"	1369	1.2%
'	1303	1.2%
,	1140	1.0%
(	1098	1.0%
)	1097	1.0%
:	943	0.8%
Other values (49)	5656	5.0%

Value	Count	Frequency (%)
e	52406	10.7%
t	41400	8.5%
o	38077	7.8%
a	36970	7.6%
n	35151	7.2%
i	34919	7.1%
r	29138	6.0%
s	26213	5.4%
l	17461	3.6%
c	17241	3.5%
Other values (42)	160258	32.8%

Value	Count	Frequency (%)
Θ	2	66.7%
π	1	33.3%

Value	Count	Frequency (%)
ܫ	1	100.0%

Value	Count	Frequency (%)
‍	3	100.0%

Value	Count	Frequency (%)
Ө	1	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	601294	> 99.9%
Punctuation	80	< 0.1%
None	8	< 0.1%
Geometric Shapes	3	< 0.1%
Syriac	1	< 0.1%
Misc Symbols	1	< 0.1%
Cyrillic	1	< 0.1%
Arrows	1	< 0.1%

Most frequent character per block

Value	Count	Frequency (%)
	89829	14.9%
e	52406	8.7%
t	41400	6.9%
o	38077	6.3%
a	36970	6.1%
n	35151	5.8%
i	34919	5.8%
r	29138	4.8%
s	26213	4.4%
l	17461	2.9%
Other values (85)	199730	33.2%

Value	Count	Frequency (%)
’	17	21.2%
”	16	20.0%
“	15	18.8%
—	11	13.8%
–	10	12.5%
‘	5	6.2%
‍	3	3.8%
…	2	2.5%
	1	1.2%

Value	Count	Frequency (%)
Θ	2	25.0%
👩	2	25.0%
π	1	12.5%
👧	1	12.5%
👦	1	12.5%
·	1	12.5%

Value	Count	Frequency (%)
◎	2	66.7%
►	1	33.3%

Value	Count	Frequency (%)
ܫ	1	100.0%

Value	Count	Frequency (%)
☺	1	100.0%

Value	Count	Frequency (%)
Ө	1	100.0%

Value	Count	Frequency (%)
→	1	100.0%

Tags
Categorical

HIGH CARDINALITY

Distinct	10083
Distinct (%)	84.5%
Missing	0
Missing (%)	0.0%
Memory size	93.4 KiB

<git>	148
<javascript>	57
<python>	41
<html><css>	32
<git><github>	30
Other values (10078)	11627

Length

Max length	102
Median length	31
Mean length	32.78583997
Min length	3

Characters and Unicode

Total characters	391299
Distinct characters	42
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	9439 ?
Unique (%)	79.1%

Sample

1st row	<c#><linq><linq-to-objects>
2nd row	<javascript><jquery><requirejs>
3rd row	<algorithm><matrix><multidimensional-array>
4th row	<sql><sql-server><tsql>
5th row	<android><github><intellij-idea><android-studio>

Value	Count	Frequency (%)
<git>	148	1.2%
<javascript>	57	0.5%
<python>	41	0.3%
<html><css>	32	0.3%
<git><github>	30	0.3%
<javascript><jquery>	28	0.2%
<docker>	27	0.2%
<python><pandas><dataframe>	23	0.2%
<javascript><arrays>	21	0.2%
<node.js>	20	0.2%
Other values (10073)	11508	96.4%

Histogram of lengths of the category

Value	Count	Frequency (%)
git	148	1.2%
javascript	57	0.5%
python	41	0.3%
html><css	32	0.3%
git><github	30	0.3%
javascript><jquery	28	0.2%
docker	27	0.2%
python><pandas><dataframe	23	0.2%
javascript><arrays	21	0.2%
regex	20	0.2%
Other values (10071)	11508	96.4%

Most occurring characters

Value	Count	Frequency (%)
<	38581	9.9%
>	38581	9.9%
a	25849	6.6%
e	25742	6.6%
t	24415	6.2%
i	23650	6.0%
o	21129	5.4%
r	20965	5.4%
n	20599	5.3%
s	20499	5.2%
Other values (32)	131289	33.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	297624	76.1%
Math Symbol	78596	20.1%
Dash Punctuation	10927	2.8%
Decimal Number	2174	0.6%
Other Punctuation	1978	0.5%

Most frequent character per category

Value	Count	Frequency (%)
a	25849	8.7%
e	25742	8.6%
t	24415	8.2%
i	23650	7.9%
o	21129	7.1%
r	20965	7.0%
n	20599	6.9%
s	20499	6.9%
c	15187	5.1%
l	12312	4.1%
Other values (16)	87277	29.3%

Value	Count	Frequency (%)
2	400	18.4%
0	337	15.5%
1	302	13.9%
3	259	11.9%
4	216	9.9%
6	190	8.7%
8	185	8.5%
5	149	6.9%
7	97	4.5%
9	39	1.8%

Value	Count	Frequency (%)
<	38581	49.1%
>	38581	49.1%
+	1434	1.8%

Value	Count	Frequency (%)
.	1356	68.6%
#	622	31.4%

Value	Count	Frequency (%)
-	10927	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	297624	76.1%
Common	93675	23.9%

Most frequent character per script

Value	Count	Frequency (%)
a	25849	8.7%
e	25742	8.6%
t	24415	8.2%
i	23650	7.9%
o	21129	7.1%
r	20965	7.0%
n	20599	6.9%
s	20499	6.9%
c	15187	5.1%
l	12312	4.1%
Other values (16)	87277	29.3%

Value	Count	Frequency (%)
<	38581	41.2%
>	38581	41.2%
-	10927	11.7%
+	1434	1.5%
.	1356	1.4%
#	622	0.7%
2	400	0.4%
0	337	0.4%
1	302	0.3%
3	259	0.3%
Other values (6)	876	0.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	391299	100.0%

Most frequent character per block

Value	Count	Frequency (%)
<	38581	9.9%
>	38581	9.9%
a	25849	6.6%
e	25742	6.6%
t	24415	6.2%
i	23650	6.0%
o	21129	5.4%
r	20965	5.4%
n	20599	5.3%
s	20499	5.2%
Other values (32)	131289	33.6%

AnswerCount
Real number (ℝ_≥0)

Distinct	118
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	15.9777964
Minimum	1
Maximum	518
Zeros	0
Zeros (%)	0.0%
Memory size	93.4 KiB

Quantile statistics

Minimum	1
5-th percentile	3
Q1	8
median	13
Q3	20
95-th percentile	37
Maximum	518
Range	517
Interquartile range (IQR)	12

Descriptive statistics

Standard deviation	14.4488636
Coefficient of variation (CV)	0.9043089075
Kurtosis	209.2596501
Mean	15.9777964
Median Absolute Deviation (MAD)	6
Skewness	8.807920863
Sum	190695
Variance	208.7696595
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
10	637	5.3%
8	636	5.3%
12	597	5.0%
7	596	5.0%
9	593	5.0%
11	579	4.9%
6	543	4.5%
5	541	4.5%
14	500	4.2%
13	481	4.0%
Other values (108)	6232	52.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	102	0.9%
2	224	1.9%
3	349	2.9%
4	452	3.8%
5	541	4.5%

Value	Count	Frequency (%)
518	1	< 0.1%
407	1	< 0.1%
320	1	< 0.1%
296	1	< 0.1%
214	1	< 0.1%

CommentCount
Real number (ℝ_≥0)

ZEROS

Distinct	46
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	3.537494763
Minimum	0
Maximum	61
Zeros	2501
Zeros (%)	21.0%
Memory size	93.4 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	2
Q3	5
95-th percentile	11
Maximum	61
Range	61
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	4.167143976
Coefficient of variation (CV)	1.177992974
Kurtosis	19.19526346
Mean	3.537494763
Median Absolute Deviation (MAD)	2
Skewness	3.121828554
Sum	42220
Variance	17.36508891
Monotocity	Not monotonic

Histogram with fixed size bins (bins=46)

Value	Count	Frequency (%)
0	2501	21.0%
1	1941	16.3%
2	1634	13.7%
3	1402	11.7%
4	1080	9.0%
5	826	6.9%
6	636	5.3%
7	482	4.0%
8	339	2.8%
9	286	2.4%
Other values (36)	808	6.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	2501	21.0%
1	1941	16.3%
2	1634	13.7%
3	1402	11.7%
4	1080	9.0%

Value	Count	Frequency (%)
61	1	< 0.1%
57	1	< 0.1%
55	1	< 0.1%
50	1	< 0.1%
47	1	< 0.1%

FavoriteCount
Real number (ℝ_≥0)

Distinct	886
Distinct (%)	7.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%

Mean	210.6356933
Minimum	81
Maximum	11317
Zeros	0
Zeros (%)	0.0%
Memory size	93.4 KiB

Quantile statistics

Minimum	81
5-th percentile	84
Q1	99
median	130
Q3	207
95-th percentile	541
Maximum	11317
Range	11236
Interquartile range (IQR)	108

Descriptive statistics

Standard deviation	339.6237681
Coefficient of variation (CV)	1.612375199
Kurtosis	303.2504586
Mean	210.6356933
Median Absolute Deviation (MAD)	39
Skewness	13.81748629
Sum	2513937
Variance	115344.3038
Monotocity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
82	203	1.7%
85	195	1.6%
81	194	1.6%
86	184	1.5%
84	179	1.5%
87	178	1.5%
88	176	1.5%
92	174	1.5%
90	174	1.5%
83	172	1.4%
Other values (876)	10106	84.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
81	194	1.6%
82	203	1.7%
83	172	1.4%
84	179	1.5%
85	195	1.6%

Value	Count	Frequency (%)
11317	1	< 0.1%
11010	1	< 0.1%
7404	1	< 0.1%
7124	1	< 0.1%
6694	1	< 0.1%

ClosedDate
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct	1369
Distinct (%)	99.6%
Missing	10560
Missing (%)	88.5%
Memory size	93.4 KiB

2020-02-02 13:32:29	3
2020-01-31 06:45:01	2
2020-01-31 06:45:02	2
2020-01-31 06:44:36	2
2020-02-02 13:30:21	2
Other values (1364)	1364

Length

Max length	19
Median length	19
Mean length	19
Min length	19

Characters and Unicode

Total characters	26125
Distinct characters	13
Distinct categories	4 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1364 ?
Unique (%)	99.2%

Sample

1st row	2011-10-07 13:02:51
2nd row	2012-02-29 16:53:12
3rd row	2018-03-28 08:29:45
4th row	2011-11-28 18:39:35
5th row	2011-10-02 14:01:25

Value	Count	Frequency (%)
2020-02-02 13:32:29	3	< 0.1%
2020-01-31 06:45:01	2	< 0.1%
2020-01-31 06:45:02	2	< 0.1%
2020-01-31 06:44:36	2	< 0.1%
2020-02-02 13:30:21	2	< 0.1%
2019-12-02 16:15:01	1	< 0.1%
2013-11-26 00:13:35	1	< 0.1%
2016-12-23 03:04:45	1	< 0.1%
2015-03-20 16:09:27	1	< 0.1%
2009-07-01 20:54:21	1	< 0.1%
Other values (1359)	1359	11.4%
(Missing)	10560	88.5%

Histogram of lengths of the category

Value	Count	Frequency (%)
2020-01-31	21	0.8%
2021-02-10	10	0.4%
2020-02-02	6	0.2%
2013-09-06	4	0.1%
2019-12-18	4	0.1%
2012-02-16	4	0.1%
2017-04-07	4	0.1%
2011-10-05	4	0.1%
2013-01-11	3	0.1%
2014-12-23	3	0.1%
Other values (2430)	2687	97.7%

Most occurring characters

Value	Count	Frequency (%)
0	4554	17.4%
1	4136	15.8%
2	3629	13.9%
-	2750	10.5%
:	2750	10.5%
3	1474	5.6%
	1375	5.3%
4	1260	4.8%
5	1200	4.6%
6	783	3.0%
Other values (3)	2214	8.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	19250	73.7%
Dash Punctuation	2750	10.5%
Other Punctuation	2750	10.5%
Space Separator	1375	5.3%

Most frequent character per category

Value	Count	Frequency (%)
0	4554	23.7%
1	4136	21.5%
2	3629	18.9%
3	1474	7.7%
4	1260	6.5%
5	1200	6.2%
6	783	4.1%
8	749	3.9%
9	745	3.9%
7	720	3.7%

Value	Count	Frequency (%)
-	2750	100.0%

Value	Count	Frequency (%)
	1375	100.0%

Value	Count	Frequency (%)
:	2750	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	26125	100.0%

Most frequent character per script

Value	Count	Frequency (%)
0	4554	17.4%
1	4136	15.8%
2	3629	13.9%
-	2750	10.5%
:	2750	10.5%
3	1474	5.6%
	1375	5.3%
4	1260	4.8%
5	1200	4.6%
6	783	3.0%
Other values (3)	2214	8.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	26125	100.0%

Most frequent character per block

Value	Count	Frequency (%)
0	4554	17.4%
1	4136	15.8%
2	3629	13.9%
-	2750	10.5%
:	2750	10.5%
3	1474	5.6%
	1375	5.3%
4	1260	4.8%
5	1200	4.6%
6	783	3.0%
Other values (3)	2214	8.5%

CommunityOwnedDate
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct	556
Distinct (%)	100.0%
Missing	11379
Missing (%)	95.3%
Memory size	93.4 KiB

2009-02-18 18:32:02	1
2013-01-22 14:48:20	1
2008-10-21 21:02:34	1
2013-09-28 05:29:12	1
2008-10-29 17:09:40	1
Other values (551)	551

Length

Max length	19
Median length	19
Mean length	19
Min length	19

Characters and Unicode

Total characters	10564
Distinct characters	13
Distinct categories	4 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	556 ?
Unique (%)	100.0%

Sample

1st row	2013-11-25 20:37:32
2nd row	2009-01-07 20:14:42
3rd row	2008-10-03 13:35:43
4th row	2009-09-14 21:00:18
5th row	2008-10-23 22:39:45

Value	Count	Frequency (%)
2009-02-18 18:32:02	1	< 0.1%
2013-01-22 14:48:20	1	< 0.1%
2008-10-21 21:02:34	1	< 0.1%
2013-09-28 05:29:12	1	< 0.1%
2008-10-29 17:09:40	1	< 0.1%
2010-02-16 18:51:14	1	< 0.1%
2008-09-30 06:28:53	1	< 0.1%
2012-11-12 21:30:59	1	< 0.1%
2009-01-11 20:02:42	1	< 0.1%
2012-08-01 15:16:15	1	< 0.1%
Other values (546)	546	4.6%
(Missing)	11379	95.3%

Histogram of lengths of the category

Value	Count	Frequency (%)
2008-09-19	16	1.4%
2010-01-22	7	0.6%
2008-10-01	5	0.4%
2008-09-24	4	0.4%
2009-01-07	4	0.4%
2008-09-17	4	0.4%
2008-09-16	4	0.4%
2008-10-17	4	0.4%
2009-01-02	3	0.3%
2008-09-15	3	0.3%
Other values (990)	1058	95.1%

Most occurring characters

Value	Count	Frequency (%)
0	2101	19.9%
1	1542	14.6%
2	1419	13.4%
-	1112	10.5%
:	1112	10.5%
	556	5.3%
3	522	4.9%
9	462	4.4%
5	424	4.0%
4	418	4.0%
Other values (3)	896	8.5%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	7784	73.7%
Dash Punctuation	1112	10.5%
Other Punctuation	1112	10.5%
Space Separator	556	5.3%

Most frequent character per category

Value	Count	Frequency (%)
0	2101	27.0%
1	1542	19.8%
2	1419	18.2%
3	522	6.7%
9	462	5.9%
5	424	5.4%
4	418	5.4%
8	370	4.8%
7	263	3.4%
6	263	3.4%

Value	Count	Frequency (%)
-	1112	100.0%

Value	Count	Frequency (%)
	556	100.0%

Value	Count	Frequency (%)
:	1112	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	10564	100.0%

Most frequent character per script

Value	Count	Frequency (%)
0	2101	19.9%
1	1542	14.6%
2	1419	13.4%
-	1112	10.5%
:	1112	10.5%
	556	5.3%
3	522	4.9%
9	462	4.4%
5	424	4.0%
4	418	4.0%
Other values (3)	896	8.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	10564	100.0%

Most frequent character per block

Value	Count	Frequency (%)
0	2101	19.9%
1	1542	14.6%
2	1419	13.4%
-	1112	10.5%
:	1112	10.5%
	556	5.3%
3	522	4.9%
9	462	4.4%
5	424	4.0%
4	418	4.0%
Other values (3)	896	8.5%

ContentLicense
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	93.4 KiB

CC BY-SA 3.0	6851
CC BY-SA 4.0	3677
CC BY-SA 2.5	1407

Length

Max length	12
Median length	12
Mean length	12
Min length	12

Characters and Unicode

Total characters	143220
Distinct characters	13
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	CC BY-SA 4.0
2nd row	CC BY-SA 3.0
3rd row	CC BY-SA 4.0
4th row	CC BY-SA 3.0
5th row	CC BY-SA 3.0

Value	Count	Frequency (%)
CC BY-SA 3.0	6851	57.4%
CC BY-SA 4.0	3677	30.8%
CC BY-SA 2.5	1407	11.8%

Histogram of lengths of the category

Value	Count	Frequency (%)
by-sa	11935	33.3%
cc	11935	33.3%
3.0	6851	19.1%
4.0	3677	10.3%
2.5	1407	3.9%

Most occurring characters

Value	Count	Frequency (%)
C	23870	16.7%
	23870	16.7%
B	11935	8.3%
Y	11935	8.3%
-	11935	8.3%
S	11935	8.3%
A	11935	8.3%
.	11935	8.3%
0	10528	7.4%
3	6851	4.8%
Other values (3)	6491	4.5%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	71610	50.0%
Space Separator	23870	16.7%
Decimal Number	23870	16.7%
Dash Punctuation	11935	8.3%
Other Punctuation	11935	8.3%

Most frequent character per category

Value	Count	Frequency (%)
C	23870	33.3%
B	11935	16.7%
Y	11935	16.7%
S	11935	16.7%
A	11935	16.7%

Value	Count	Frequency (%)
0	10528	44.1%
3	6851	28.7%
4	3677	15.4%
2	1407	5.9%
5	1407	5.9%

Value	Count	Frequency (%)
	23870	100.0%

Value	Count	Frequency (%)
-	11935	100.0%

Value	Count	Frequency (%)
.	11935	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	71610	50.0%
Common	71610	50.0%

Most frequent character per script

Value	Count	Frequency (%)
	23870	33.3%
-	11935	16.7%
.	11935	16.7%
0	10528	14.7%
3	6851	9.6%
4	3677	5.1%
2	1407	2.0%
5	1407	2.0%

Value	Count	Frequency (%)
C	23870	33.3%
B	11935	16.7%
Y	11935	16.7%
S	11935	16.7%
A	11935	16.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	143220	100.0%

Most frequent character per block

Value	Count	Frequency (%)
C	23870	16.7%
	23870	16.7%
B	11935	8.3%
Y	11935	8.3%
-	11935	8.3%
S	11935	8.3%
A	11935	8.3%
.	11935	8.3%
0	10528	7.4%
3	6851	4.8%
Other values (3)	6491	4.5%

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	Id	PostTypeId	AcceptedAnswerId	ParentId	CreationDate	DeletionDate	Score	ViewCount	Body	OwnerUserId	OwnerDisplayName	LastEditorUserId	LastEditorDisplayName	LastEditDate	LastActivityDate	Title	Tags	AnswerCount	CommentCount	FavoriteCount	ClosedDate	CommunityOwnedDate	ContentLicense
0	41244	1	233505.0	NaN	2008-09-03 06:30:31	NaN	690	283067	<p>I found an example in the <a href="http://msdn2.microsoft.com/en-us/bb330936.aspx" rel="noreferrer">VS2008 Examples</a> for Dynamic LINQ that allows you to use a sql-like string (e.g. <code>OrderBy("Name, Age DESC"))</code> for ordering. Unfortunately, the method included only works on <code>IQueryable<T></code>. Is there any way to get this functionality on <code>IEnumerable<T></code>?</p>\n	1786.0	John Sheehan	5519709.0	John Sheehan	2020-05-06 08:43:38	2020-09-27 11:39:24	Dynamic LINQ OrderBy on IEnumerable<T> / IQueryable<T>	<c#><linq><linq-to-objects>	20	1	440	NaN	NaN	CC BY-SA 4.0
1	8315088	1	8479953.0	NaN	2011-11-29 17:30:29	NaN	302	108680	<p>RequireJS seems to do something internally that caches required javascript files. If I make a change to one of the required files, I have to rename the file in order for the changes to be applied. </p>\n\n<p>The common trick of appending a version number as a querystring param to the end of the filename does not work with requirejs <code><script src="jsfile.js?v2"></script></code> </p>\n\n<p>What I am looking for is a way to prevent this internal cacheing of RequireJS required scripts without having to rename my script files every time they are updated.</p>\n\n<p><strong>Cross-Platform Solution:</strong></p>\n\n<p>I am now using <code>urlArgs: "bust=" + (new Date()).getTime()</code> for automatic cache-busting during development and <code>urlArgs: "bust=v2"</code> for production where I increment the hard-coded version num after rolling out an updated required script.</p>\n\n<p><strong>Note:</strong></p>\n\n<p>@Dustin Getz mentioned in a recent answer that Chrome Developer Tools will drop breakpoints during debugging when Javascript files are continuously refreshed like this. One workaround is to write <code>debugger;</code> in code to trigger a breakpoint in most Javascript debuggers.</p>\n\n<p><strong>Server-Specific Solutions:</strong></p>\n\n<p>For specific solutions that may work better for your server environment such as Node or Apache, see some of the answers below.</p>\n	285714.0	NaN	285714.0	NaN	2013-12-11 17:09:20	2018-09-12 07:43:01	Prevent RequireJS from Caching Required Scripts	<javascript><jquery><requirejs>	12	6	116	NaN	NaN	CC BY-SA 3.0
2	42519	1	42535.0	NaN	2008-09-03 20:34:33	NaN	323	333094	<p>Inspired by <a href="https://devblogs.microsoft.com/oldnewthing/20080902-00/?p=21003" rel="noreferrer">Raymond Chen's post</a>, say you have a 4x4 two dimensional array, write a function that rotates it 90 degrees. Raymond links to a solution in pseudo code, but I'd like to see some real world stuff.</p>\n\n<pre><code>[1][2][3][4]\n[5][6][7][8]\n[9][0][1][2]\n[3][4][5][6]\n</code></pre>\n\n<p>Becomes:</p>\n\n<pre><code>[3][9][5][1]\n[4][0][6][2]\n[5][1][7][3]\n[6][2][8][4]\n</code></pre>\n\n<p><strong>Update</strong>: Nick's answer is the most straightforward, but is there a way to do it better than n^2? What if the matrix was 10000x10000?</p>\n	736.0	swilliams	4415734.0	Jakub Šturc	2020-02-22 17:25:33	2020-12-16 18:32:49	How do you rotate a two dimensional array?	<algorithm><matrix><multidimensional-array>	63	13	221	NaN	2013-11-25 20:37:32	CC BY-SA 4.0
3	42648	1	42655.0	NaN	2008-09-03 21:32:02	NaN	1202	953268	<p>What is the best way to get <code>IDENTITY</code> of inserted row?</p>\n\n<p>I know about <code>@@IDENTITY</code> and <code>IDENT_CURRENT</code> and <code>SCOPE_IDENTITY</code> but don't understand the pros and cons attached to each.</p>\n\n<p>Can someone please explain the differences and when I should be using each?</p>\n	1583.0	Oded	3876565.0	NaN	2018-03-26 08:14:43	2021-01-14 02:35:41	Best way to get identity of inserted row?	<sql><sql-server><tsql>	14	4	348	NaN	NaN	CC BY-SA 3.0
4	16644946	1	16682570.0	NaN	2013-05-20 07:53:02	NaN	210	321230	<p>I am trying to synchronise a project that I have on in my Android Studio folder to GitHub, but I am not fully sure what to do other than adding my credentials in the options menu. Could someone give me a quick guide, please?</p>\n	1342580.0	NaN	63550.0	NaN	2016-09-16 04:18:58	2021-02-28 21:59:41	How do you synchronise projects to GitHub with Android Studio?	<android><github><intellij-idea><android-studio>	13	0	95	NaN	NaN	CC BY-SA 3.0
5	45015	1	5686237.0	NaN	2008-09-05 00:12:01	NaN	1393	1312306	<p>Given a string of JSON data, how can I safely turn that string into a JavaScript object?</p>\n\n<p>Obviously I can do this unsafely with something like:</p>\n\n<pre><code>var obj = eval("(" + json + ')');\n</code></pre>\n\n<p>but that leaves me vulnerable to the JSON string containing other code, which it seems very dangerous to simply eval.</p>\n	797.0	Matt Sheppard	860099.0	NaN	2020-09-21 12:15:56	2020-09-21 12:15:56	Safely turning a JSON string into an object	<javascript><json><parsing>	28	5	224	NaN	NaN	CC BY-SA 4.0
6	10564	1	773973.0	NaN	2008-08-14 01:43:04	NaN	585	341505	<p>I'm trying out <strong>Git on Windows</strong>. I got to the point of trying "git commit" and I got this error:</p>\n\n<blockquote>\n <p>Terminal is dumb but no VISUAL nor\n EDITOR defined. Please supply the\n message using either -m or -F option.</p>\n</blockquote>\n\n<p>So I figured out I need to have an environment variable called EDITOR. No problem. I set it to point to Notepad. That worked, almost. The default commit message opens in Notepad. But Notepad doesn't support bare line feeds. I went out and got <a href="http://notepad-plus.sourceforge.net/uk/site.htm" rel="noreferrer">Notepad++</a>, but I can't figure out how to get Notepad++ set up as the <code>%EDITOR%</code> in such a way that it works with Git as expected.</p>\n\n<p>I'm not married to Notepad++. At this point I don't mind what editor I use. I just want to be able to <strong>type commit messages in an editor</strong> rather than the command line (with <code>-m</code>).</p>\n\n<p>Those of you using Git on Windows: What tool do you use to edit your commit messages, and what did you have to do to make it work?</p>\n	437.0	Patrick McElhaney	1002260.0	ΤΖΩΤΖΙΟΥ	2016-12-24 17:26:04	2020-12-18 10:49:47	How can I set up an editor to work with Git on Windows?	<windows><git><cygwin><editor>	34	6	289	NaN	NaN	CC BY-SA 2.5
7	24965	1	25203.0	NaN	2008-08-24 10:43:17	NaN	43	20579	<p>OK, I know there have already been questions about <a href="https://stackoverflow.com/questions/4303/why-should-i-practice-test-driven-development-and-how-should-i-start">getting started with TDD</a>.. However, I guess I kind of know the general concensus is to <em>just do it</em> , However, I seem to have the following problems getting my head into the game:</p>\n\n<ul>\n<li>When working with collections, do will still test for obvious add/remove/inserts successful, even when based on Generics etc where we kind of "know" its going to work?</li>\n<li>Some tests seem to take forever to implement.. Such as when working with string output, is there a "better" way to go about this sort of thing? (e.g. test the object model before parsing, break parsing down into small ops and test there) In my mind you should always test the "end result" but that can vary wildly and be tedious to set up.</li>\n<li>I don't have a testing framework to use (work wont pay for one) so I can "practice" more. Are there any good ones that are free for commercial use? (at the moment I am using good 'ol <em>Debug.Assert</em> :)</li>\n<li>Probably the biggest.. Sometimes I don't know what to expect <em>NOT</em> to happen.. I mean, you get your green light but I am always concerned that I may be missing a test.. Do you dig deeper to try and break the code, or leave it be and wait for it all fall over later (which will cost more)..</li>\n</ul>\n\n<p>So basically what I am looking for here is not a " <em>just do it</em> " but more " <em>I did this, had problems with this, solved them by this</em> ".. The <strong>personal</strong> experience :)</p>\n	832.0	Rob Cooper	-1.0	Chris Fournier	2017-05-23 12:34:45	2011-08-27 20:33:46	Beginning TDD - Challenges? Solutions? Recommendations?	<unit-testing><language-agnostic><tdd>	11	0	117	2011-10-07 13:02:51	NaN	CC BY-SA 2.5
8	26255	1	60396.0	NaN	2008-08-25 15:29:39	NaN	163	68763	<p>What JavaScript keywords (function names, variables, etc) are reserved?</p>\n	399.0	Titanous	122643.0	NaN	2018-01-15 00:16:53	2021-01-28 11:57:03	Reserved keywords in JavaScript	<javascript><reserved-words>	8	1	94	NaN	NaN	CC BY-SA 3.0
9	58306	1	58446.0	NaN	2008-09-12 04:36:51	NaN	117	98489	<p>I am trying to determine the best time efficient algorithm to accomplish the task described below.</p>\n\n<p>I have a set of records. For this set of records I have connection data which indicates how pairs of records from this set connect to one another. This basically represents an undirected graph, with the records being the vertices and the connection data the edges.</p>\n\n<p>All of the records in the set have connection information (i.e. no orphan records are present; each record in the set connects to one or more other records in the set).</p>\n\n<p>I want to choose any two records from the set and be able to show all simple paths between the chosen records. By "simple paths" I mean the paths which do not have repeated records in the path (i.e. finite paths only).</p>\n\n<p>Note: The two chosen records will always be different (i.e. start and end vertex will never be the same; no cycles).</p>\n\n<p>For example:</p>\n\n<pre>\n If I have the following records:\n A, B, C, D, E\n\n and the following represents the connections: \n (A,B),(A,C),(B,A),(B,D),(B,E),(B,F),(C,A),(C,E),\n (C,F),(D,B),(E,C),(E,F),(F,B),(F,C),(F,E)\n\n [where (A,B) means record A connects to record B]\n</pre>\n\n<p>If I chose B as my starting record and E as my ending record, I would want to find all simple paths through the record connections that would connect record B to record E.</p>\n\n<pre>\n All paths connecting B to E:\n B->E\n B->F->E\n B->F->C->E\n B->A->C->E\n B->A->C->F->E\n</pre>\n\n<p>This is an example, in practice I may have sets containing hundreds of thousands of records.</p>\n	3534.0	Robert	1571709.0	cynicalman	2017-03-23 00:30:31	2017-03-23 00:30:31	Graph Algorithm To Find All Connections Between Two Arbitrary Vertices	<algorithm><language-agnostic><graph-theory>	16	3	94	NaN	NaN	CC BY-SA 3.0

Last rows

	Id	PostTypeId	AcceptedAnswerId	ParentId	CreationDate	DeletionDate	Score	ViewCount	Body	OwnerUserId	OwnerDisplayName	LastEditorUserId	LastEditorDisplayName	LastEditDate	LastActivityDate	Title	Tags	AnswerCount	CommentCount	FavoriteCount	ClosedDate	CommunityOwnedDate	ContentLicense
11925	41060382	1	43729857.0	NaN	2016-12-09 12:20:55	NaN	245	414074	<p>conda 4.2.13\nMacOSX 10.12.1</p>\n\n<p>I am trying to install packages from <code>pip</code> to a fresh environment (virtual) created using anaconda. <a href="http://conda.pydata.org/docs/using/pkgs.html#install-non-conda-packages" rel="noreferrer">In the Anaconda docs</a> it says this is perfectly fine. It is done the same way as for virtualenv. </p>\n\n<blockquote>\n <p>Activate the environment where you want to put the program, then pip install a program...</p>\n</blockquote>\n\n<p>I created an empty environment in Ananconda like this:</p>\n\n<pre><code>conda create -n shrink_venv\n</code></pre>\n\n<p>Activate it:</p>\n\n<pre><code>source activate shrink_venv\n</code></pre>\n\n<p>I then can see in the terminal that I am working in my env <code>(shrink_venv)</code>. Problem is coming up, when I try to install a package using <code>pip</code>:</p>\n\n<pre><code>(shrink_venv): pip install Pillow\n\nRequirement already satisfied (use --upgrade to upgrade): Pillow in /Library/Python/2.7/site-packages\n</code></pre>\n\n<p>So I can see it thinks the requirement is satisfied from the system-wide package. So it seems the environment is not working correctly, definitely not like it said in the docs. Am I doing something wrong here?</p>\n\n<p>Just a note, I know you can use <code>conda install</code> for the packages, but I have had an issue with Pillow from anaconda, so I wanted to get it from <code>pip</code>, and since the docs say that is fine.</p>\n\n<p>Output of <code>which -a pip</code>:</p>\n\n<pre><code>/usr/local/bin/pip\n/Users/my_user/anaconda/bin/pip\n</code></pre>\n\n<p> UPDATE \nI see this is pretty common issue. What I have found is that the conda env doesn't play well with the PYTHONPATH. The system seems to always look in the PYTHONPATH locations even when you're using a conda environment. Now, I always run <code>unset PYTHONPATH</code> when using a conda environment, and it works much better. I'm on a mac.</p>\n	959306.0	NaN	959306.0	NaN	2018-10-24 14:02:24	2021-02-10 19:20:52	Using Pip to install packages to Anaconda Environment	<python><pip><anaconda><environment>	15	10	131	NaN	NaN	CC BY-SA 4.0
11926	40801772	1	NaN	NaN	2016-11-25 09:39:10	NaN	609	307159	<p>What is the difference between <code>ports</code> and <code>expose</code> options in <code>docker-compose.yml</code></p>\n	84143.0	NaN	4516110.0	NaN	2019-02-26 09:47:22	2021-04-10 03:07:19	What is the difference between docker-compose ports vs expose	<docker><docker-compose>	5	0	133	NaN	NaN	CC BY-SA 4.0
11927	40987309	1	40988672.0	NaN	2016-12-06 03:23:50	NaN	184	323838	<p>This is an example from Google Adsense application page. The loading screen displayed before the main page showed after.</p>\n\n<p><a href="https://i.stack.imgur.com/ngk1r.gif" rel="noreferrer"><img src="https://i.stack.imgur.com/ngk1r.gif" alt="enter image description here"></a></p>\n\n<p>I don't know how to do the same thing with React because if I make loading screen rendered by React component, it doesn't display while page is loading because it has to wait for DOM rendered before.</p>\n\n<p><strong>Updated</strong>:</p>\n\n<p>I made an example of my approach by putting screen loader in <code>index.html</code> and remove it in React <code>componentDidMount()</code> lifecycle method.</p>\n\n<p><a href="https://nguyenbathanh.github.io" rel="noreferrer">Example</a> and <a href="https://github.com/nguyenbathanh/react-loading-screen" rel="noreferrer">react-loading-screen</a>.</p>\n	1550476.0	NaN	11673149.0	NaN	2019-08-17 08:10:24	2020-11-13 05:16:38	React - Display loading screen while DOM is rendering?	<reactjs><asynchronous><redux>	19	3	99	NaN	NaN	CC BY-SA 4.0
11928	40703675	1	40704083.0	NaN	2016-11-20 11:51:03	NaN	193	41750	<p>Came to know that from <strong>React v15.3.0</strong>, we have a new base class called <strong>PureComponent</strong> to extend with <strong>PureRenderMixin</strong> built-in. What I understand is that, under the hood this employs a shallow comparison of props inside <code>shouldComponentUpdate</code>.</p>\n<p>Now we have 3 ways to define a React component:</p>\n<ol>\n<li>Functional stateless component which doesn't extend any class</li>\n<li>A component that extends <code>PureComponent</code> class</li>\n<li>A normal component that extends <code>Component</code> class</li>\n</ol>\n<p>Some time back we used to call stateless components as Pure Components, or even Dumb Components. Seems like the whole definition of the word "pure" has now changed in React.</p>\n<p>Although I understand basic differences between these three, I am still not sure <strong>when to choose what</strong>. Also what are the performance impacts and trade-offs of each?</p>\n<hr />\n<h3><strong>Update</strong>:</h3>\n<p>These are the question I expect to get clarified:</p>\n<ul>\n<li>Should I choose to define my simple components as functional (for the sake of simplicity) or extend <code>PureComponent</code> class (for performance sake)?</li>\n<li>Is the performance boost that I get a real trade-off for the\nsimplicity I lost?</li>\n<li>Would I ever need to extend the normal <code>Component</code> class when I can always use <code>PureComponent</code> for better performance?</li>\n</ul>\n	5069226.0	NaN	-1.0	NaN	2020-06-20 09:12:55	2019-02-15 18:01:24	React functional stateless component, PureComponent, Component; what are the differences and when should we use what?	<javascript><reactjs><ecmascript-6>	3	0	120	NaN	NaN	CC BY-SA 3.0
11929	40729535	1	50255986.0	NaN	2016-11-21 21:15:29	NaN	374	101834	<p>I'm trying out the new VS 2017 RC and wondering if anyone knows how to get the previous debugging behavior back</p>\n\n<p>In VS 2015 it went like this:</p>\n\n<p>Press start debugging</p>\n\n<ul>\n<li>Website opens in new Chrome tab</li>\n<li>Press stop debugging</li>\n<li>Website is still open and the site is still running/active</li>\n</ul>\n\n<p>Now in 2017:</p>\n\n<ul>\n<li>Press start debugging</li>\n<li>Website opens in new window that can't dock with any other Chrome windows/tabs</li>\n<li>Press stop debugging</li>\n<li>Website/Chrome window closes, can't continue using the site unless I manually go to the localhost window in Chrome</li>\n</ul>\n\n<p>Is it possible in 2017 to switch back to the 2015 style? So the Chrome/Website window can dock with other Chrome windows/tabs, and it stays open after you stop debugging?</p>\n\n<p>Additionally, I find the new Chrome window frustrating to use, as it seems not to have any history/content available. E.g I can't autocomplete forms or urls, which is very annoying when I'm trying to test a form</p>\n	290328.0	NaN	290328.0	NaN	2016-11-22 03:00:33	2019-05-03 19:15:37	How to stop browser closing automatically when you stop debugging on VS 2017	<visual-studio><visual-studio-2017>	6	0	81	NaN	NaN	CC BY-SA 3.0
11930	21194934	1	26815894.0	NaN	2014-01-17 20:08:41	NaN	804	576258	<p>Is this the right way to create a directory if it doesn't exist.\nIt should have full permission for the script and readable by others.</p>\n\n<pre><code>var dir = __dirname + '/upload';\nif (!path.existsSync(dir)) {\n fs.mkdirSync(dir, 0744);\n}\n</code></pre>\n	356380.0	NaN	7080548.0	NaN	2019-10-29 14:40:12	2020-12-03 17:43:31	How to create a directory if it doesn't exist using Node.js?	<node.js>	19	2	98	NaN	NaN	CC BY-SA 4.0
11931	48910876	1	51024493.0	NaN	2018-02-21 16:30:30	NaN	449	387510	<p>What might be causing the error <code>Error: EACCES: permission denied, access '/usr/local/lib/node_modules'</code>?</p>\n\n<pre><code>npm ERR! path /usr/local/lib/node_modules\nnpm ERR! code EACCES\nnpm ERR! errno -13\nnpm ERR! syscall access\nnpm ERR! Error: EACCES: permission denied, access '/usr/local/lib/node_modules'\nnpm ERR! { Error: EACCES: permission denied, access '/usr/local/lib/node_modules'\nnpm ERR! errno: -13,\nnpm ERR! code: 'EACCES',\nnpm ERR! syscall: 'access',\nnpm ERR! path: '/usr/local/lib/node_modules' }\nnpm ERR! \nnpm ERR! Please try running this command again as root/Administrator.\n\nnpm ERR! A complete log of this run can be found in:\nnpm ERR! /Users/macbookmd101/.npm/_logs/2018-02-21T16_26_08_421Z-debug.log\n</code></pre>\n	9322906.0	NaN	2745495.0	NaN	2020-08-24 09:40:32	2021-02-19 14:13:57	Error: EACCES: permission denied, access '/usr/local/lib/node_modules'	<node.js><npm><permission-denied>	34	1	120	NaN	NaN	CC BY-SA 4.0
11932	13813254	1	13813255.0	NaN	2012-12-11 03:39:53	NaN	313	539380	<p>Q1. Suppose I want to alter the look of each "item" that a user marks for deletion before the main "delete" button is pressed. (This immediate visual feedback should eliminate the need for the proverbial "are you sure?" dialog box.) The user will check checkboxes to indicate which items should be deleted. If a checkbox is unchecked, that item should revert back to its normal look.</p>\n\n<p>What's the best way to apply or remove the CSS styling?</p>\n\n<p>Q2. Suppose I want to allow each user to personalize how my site is presented. E.g., select from a fixed set of font sizes, allow user-definable foreground and background colors, etc.</p>\n\n<p>What's the best way to apply the CSS styling the user selects/inputs?</p>\n	215945.0	NaN	NaN	NaN	NaN	2019-02-08 15:14:05	How do I conditionally apply CSS styles in AngularJS?	<css><angularjs>	14	1	160	NaN	NaN	CC BY-SA 3.0
11933	33022662	1	33023788.0	NaN	2015-10-08 17:48:07	NaN	160	66470	<p>I want to build an app that centers around getting the user's current location and then find points of interest(such as bars,restaurants,etc) that are close to him/her via the <strong>Google Places API</strong>. </p>\n\n<p>Upon searching the web for a place to start I came across some tutorials that use the <code>LocationManager</code> class and some others that use <strong>Google Play Services</strong> in order to find the users location.</p>\n\n<p>On first sight both of them do the same thing, but since I am new to this I got a little confused and I don't know which method suits my needs the best. So, I want to ask you :</p>\n\n<p><strong>What are the differences between these two methods of finding locations (if there are any) ?</strong></p>\n	2740158.0	NaN	NaN	NaN	NaN	2021-03-09 14:01:16	Android : LocationManager vs Google Play Services	<android><gps><geolocation>	8	1	84	NaN	NaN	CC BY-SA 3.0
11934	40823315	1	40824044.0	NaN	2016-11-26 21:24:32	NaN	237	77048	<p>There are already several Q&As on this "<em>X does not implement Y (... method has a pointer receiver)</em>" thing, but to me, they seems to be talking about different things, and not applying to my specific case.</p>\n\n<p>So, instead of making the question very specific, I'm making it broad and abstract -- Seems like there are several different cases that can make this error happen, can someone summary it up please?</p>\n\n<p>I.e., how to avoid the problem, and if it occurs, what are the possibilities? Thx.</p>\n	2125837.0	NaN	13860.0	NaN	2018-09-10 16:40:08	2021-03-19 08:26:45	X does not implement Y (... method has a pointer receiver)	<pointers><go><methods><interface>	4	0	81	NaN	NaN	CC BY-SA 4.0

Overview

Variables

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Most occurring characters